Knowledge Alchemy: Models and Agency in Global Knowledge Governance 9781529214420

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Knowledge Alchemy  Tero Erkkilä, Meng-Hsuan Chou and Niilo Kauppi

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Knowledge Alchemy Models and Agency in Global Knowledge Governance

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Tero Erkkilä, Meng-Hsuan Chou and Niilo Kauppi

ISBN: 978-1-5292-0034-8 B R I S TO L

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@bristoluniversitypress www.bristoluniversitypress.co.uk



Transnational Administration and Global Policy Series Editors: Kim Moloney, College of Public Policy, Hamad Bin Khalifa University, Qatar, Michael W. Bauer, School of Transnational Governance, European University Institute, Italy and Meng-Hsuan Chou, NTU Singapore This series contributes to the theorization of the expansion and institutionalization of global-governance structures by examining the actors and ideas permeating, shaping and engaging in these developments. Also available Civil Servants and Globalization Integrating MENA Countries in a Globalized Economy By Tony Verheijen, Katarina Staronova, Ibrahim Elghandour and Anne-Lucie Lefebvre Forthcoming Whistleblowing and Retaliation in the United Nations Edited by Caroline Hunt-Matthes and Alexis Bushnell Administering Global Health How the Pragmatic and Strategic Business of Doing Global Health Shapes Priorities and Policies By Carmen Huckel Schneider Ending Gender-Based Violence The Global Governance of Harmful Practices By Laura Rahm

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International Advisory Board Christoph Knill, Ludwig-Maximilians-University, Germany Tom Kompas, University of Melbourne, Australia Jonathan Koppell, Arizona State University, US Miguel Poiares Maduro, European University Institute, Italy Muna Ndulo, Cornell University, US Leslie A. Pal, Hamad Bin Khalifa University, Qatar Ruth Rubio Marín, Universidad de Sevilla, Spain and European University Institute, Italy Diane Stone, European Union Institute, Italy Jarle Trondal, University of Agder and ARENA Centre for European Studies, University of Oslo, Norway Diana Tussie, FLACSO, Argentina Steven Van de Walle, KU Leuven, Belgium

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KNOWLEDGE ALCHEMY Models and Agency in Global Knowledge Governance Tero Erkkilä, Meng-​Hsuan Chou and Niilo Kauppi

First published in Great Britain in 2023 by Bristol University Press University of Bristol 1–​9 Old Park Hill Bristol BS2 8BB UK t: +​44 (0)117 374 6645 e: bup-​[email protected] Details of international sales and distribution partners are available at bristoluniversitypress.co.uk © Bristol University Press 2023 British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library ISBN 978-1-5292-1440-6 hardcover ISBN 978-1-5292-1441-3 ePub ISBN 978-1-5292-1442-0 ePdf The right of Tero Erkkilä, Meng-​Hsuan Chou and Niilo Kauppi to be identified as authors of this work has been asserted by them in accordance with the Copyright, Designs and Patents Act 1988. All rights reserved: no part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise without the prior permission of Bristol University Press. Every reasonable effort has been made to obtain permission to reproduce copyrighted material. If, however, anyone knows of an oversight, please contact the publisher. The statements and opinions contained within this publication are solely those of the authors and not of the University of Bristol or Bristol University Press. The University of Bristol and Bristol University Press disclaim responsibility for any injury to persons or property resulting from any material published in this publication. Bristol University Press works to counter discrimination on grounds of gender, race, disability, age and sexuality. Cover design: blu inc Front cover image: Unsplash/Pawel Czerwinski Bristol University Press use environmentally responsible print partners. Printed and bound in Great Britain by CPI Group (UK) Ltd, Croydon, CR0 4YY

Contents List of Figures and Tables List of Abbreviations Preface

vi vii ix

1 Introduction

1

PART I Indicators, Data and Models of Global Knowledge Governance 2 Global Rankings of Good Governance and Higher Education 21 3 Human Capital and the Rise of the Global Talent 46 Competition PART II Scripts, Imaginaries and Policy 4 Global Imaginaries of Knowledge Governance 5 From the Medieval Scholar to the Global Flows of Academics: Exploring the Emergence, Evolution and Impact of the ‘Talent’ Imaginary 6 Strategies and Policies for the Global Talent Competition

120

7 Conclusion

143

References Index

154 189

v

77 94

List of Figures and Tables Figures 3.1 5.1

Network of data sources (by producer), GCI, GII, GTCI, GPCI Singapore’s non-​resident population (2012–​2022)

55 110

Tables 1.1 2.1 2.2 2.3 2.4 2.5 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 5.1 5.2 6.1

The two phases of knowledge alchemy Global indicators of competitiveness, governance, higher education, human capital and innovation Rankings and second-​generation indicators as policy instruments Selected global university rankings Non-​aggregated indicators of higher education References to international rankings in 2014 and 2018 Competitiveness assessments: city and regional level Innovation rankings for regional and city level Indicators of education and human capital in GII, GCI, GTCI and GPCI  Indicators of mobility in GII, GCI, GTCI and GPCI  Indicators of innovation in GII, GCI, GTCI and GPCI Human capital, well-​being and digital governance indicators Composition of selected human capital, well-​being and AI indicators INSEAD position in Financial Times global rankings GTCI top ten ranked countries in 2020 Singapore’s population breakdown (in thousands and percentages) Singapore’s universities in THE and QS university rankings (2011–​2023) Rankings of selected countries and innovation hubs

vi

10 23 29 31 37 40 51 52 57 59 60 61 63 67 68 109 111 137

List of Abbreviations AHELO AI AIG AIRI ARWU AUA BLI CEA CEO CNRS EU GAI GCAIRI GCI GDP GII GPCI GTCI HCI HCLI IDEX INSEAD KAU NGO NTU NUS OECD PISA R&D THE THES UASR

Assessment of Higher Education Learning Outcomes artificial intelligence anticipatory innovation governance Government AI Readiness Index Academic Ranking of World Universities Asian Universities Alliance Better Life Index Commissaire à l’énergie atomique chief executive officer Centre National de la Recherche Scientifique European Union Global AI Index Global Cities AI Readiness Index Global Competitiveness Index gross domestic product Global Innovation Index Global Power City Index Global Talent Competitiveness Index Human Capital Index Human Capital Leadership Institute Initiative d’excellence Institut Européen d’Administration des Affaires King Abdulaziz University non-​governmental organization Nanyang Technological University National University of Singapore Organisation for Economic Co-​operation and Development Program for International Student Assessment research and development Times Higher Education Times Higher Education Supplement University Alliance of the Silk Road vii

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UNCTAD UNESCO VP WEF WGI WIPO

United Nations Conference on Trade and Development United Nations Educational, Scientific and Cultural Organization Vice President World Economic Forum Worldwide Governance Indicators World Intellectual Property Organization

viii

Preface Building on our previous collaboration on higher education governance, global knowledge governance, governance indicators, university rankings, talent migration and transnational elites, the idea for this book took shape in the usual way, during a casual conversation over coffee. We were intrigued by what our observations in our individual research tell us about the state of knowledge governance. While working on the book, we have presented our ongoing research at seminars and various academic conferences, including the European Consortium for Political Research (2021), the International Studies Association (2019, 2018, 2015), the Finnish Political Science Association (2018), the European Forum for the Studies of Policies for Research and Innovation (2017), the European Science Foundation (2016, 2014) and the conferences of the European Educational Research Association (2016) and the Association européenne de l’éducation (2014). The theme of this book is knowledge alchemy –​a generic process of transforming mundane practices and policies of knowledge governance into competitive ones following imagined global gold standards and universal symbolic formulas. Such value-​producing global models now widely inform national and institutional policies and practices on global competitiveness, higher education and innovation. We have been particularly interested in the imaginary of global competition and the global talent competition paradigm and related policy models. This book provides critical accounts of policy actors’ limited agency and of the impact numerical policy scripts carried by ‘global data’ have on decision-​making. The narrative elements of rankings and indicators are crucial to an understanding of the forces that shape the globe today. Numbers tell stories. Revealingly, the French words compter (to count) and conter (to tell a story) have a similar pronunciation, sharing a Latin root (computare, to count). Narratives carried by numbers have strong temporal elements and the numerical global knowledge governance is increasingly becoming future-​ oriented. Interestingly, instead of seeing the future as an open horizon of alternatives, historical narratives often project past experiences onto an assumed future. Global ranking producers now highlight education and

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innovation –​the ability to grow and attract talent –​as remedies for the future challenges of digitization and automation by algorithms. During these years, several colleagues have helped us in our work and shaped our thinking. We would like to thank especially Dorota Dakowska, Isaac Kamola, Jim Mittelman, Romuald Normand and Susan Robertson for their support and numerous constructive comments. Thank you also to Johanna Metsänheimo, Juho Mölsä and Henrik Peiponen for invaluable research assistance. We would also like to thank our editors at Bristol University Press, Zoe Forbes and Stephen Wenham, for their help and input at critical moments of the production process. We are also grateful for the comments and critique from our anonymous reviewers. The Academy of Finland (grant numbers 268181, 345950 and 345953) and University of Helsinki Faculty of Social Sciences provided research funding for Tero Erkkilä. The Academy of Finland (grant number 2100002821, 2015–​19) and the University of Strasbourg Institute for Advanced Study (2014–​2016) provided funding for Niilo Kauppi’s research. Meng-​Hsuan Chou received funding from the University of Helsinki as a visiting professor at the Faculty of Social Sciences (2019–​2021). Most importantly, we would like to thank our families for their love and support. Tero Erkkilä, Meng-Hsuan Chou and Niilo Kauppi Helsinki and Singapore, February 2023

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1

Introduction Introduction This book is about knowledge alchemy –​a generic process of transforming mundane practices and policies of knowledge governance into competitive ones following imagined global gold standards and universal symbolic formulas. We argue and show that knowledge alchemy is prevalent around the world, informing national and institutional policies and practices on global competitiveness, higher education and innovation. Given how interdependent the world remains, knowledge alchemy is also embedded in transnational administration and steers global policy making. To understand contemporary national and transnational governance, it is thus essential to know how knowledge alchemy unfolds across multiple policy domains and sectors. Over the past few decades, there has been a surge of global rankings and indicators, resulting in quantification and numerical comparisons of various domains. Global ranking producers now highlight education and innovation as remedies for future challenges of digitization and automation by algorithms. Knowledge governance –​the process of steering and governing state information –​has been identified as essential in ensuring national economic competitiveness. As we highlight in our analysis, global knowledge governance is strongly future-​oriented and anticipatory, but somewhat paradoxically builds on historical analogies where the assumed medieval patterns of academic mobility, professionalization, city-​states, cartography and navigation are now projected onto expected futures. While imagining the future based on past developments may seem commonplace and expected, we argue that contemporary knowledge governance presupposes a transmutation process based on leaps of imagination. We critically analyse these processes through another medieval analogy, the practice of alchemy. According to one dictionary definition, alchemy is ‘the medieval combination of chemistry, philosophy, and 1

Knowledge Alchemy

secret lore aimed at transmuting base metals into gold (by means of the philosopher’s stone), and discovering the universal cure for disease and mortality’ (Blackburn 2016). As a precursor of modern chemistry (Rey 2018), alchemy aimed at defining universal formulas for transforming somewhat worthless materials into gold. Such transmutation was the ultimate goal of alchemists, the craftsmen of this trade. The work of alchemists involved many elements resembling the craftmanship of chemistry, but without systematic rigour and modern scientific knowledge. Yet, early forms of chemistry, similar to alchemy, had connotations of hurrying or carrying out God’s work (Knight 2013). While the role of alchemy in the history of science is increasingly acknowledged (Principe 2011), it still serves as a metaphor for muddling through, conjuring tricks, wishful thinking, or quasi-​science (for example, Hutson 2018). While alchemists like Sir Isaac Newton failed in their endeavours to transform lead into gold (cf Newman 2018), alchemy as a process of transformation or transmutation of value is ubiquitous to social life and human existence. We see alchemy as metaphor for a generic process of valuing and transforming existing valuations. Society and the world polity are defined by constant struggles to determine the value or worth of individuals and institutions. By using this medieval-​era concept, we wish to highlight the surprising taken-​for-​g ranted nature of historical concepts and metaphors in explaining present and future challenges of knowledge governance at both the national and transnational levels. Alchemy involves the creation of value from nothing, as in the case of money or financial assets (Soros 2003), or from one form of knowledge to another, as in digitalization and monetization when bits are transmuted into money. Valuation takes place through classifications when a dividing line as a qualitative distinction is created between the excellent and gifted who pass an exam and the losers who fail, or when certain types of data or quantitative information, such as for instance English language research outputs or teacher/​student ratios as academic benchmarks, are favoured over other types of data. These practices produce social value and determine the present worth of various objects, individuals and practices; the expectations that are bestowed on them through this valuation process then in turn determine their future worth in contemporary eyes. We do not use the concept of alchemy in pejorative terms for criticizing the work of the organizations and individuals analysed. Instead, we wish to point out the rarely acknowledged limitations of indicator-​based assessments and attempts to anticipate the future. Indeed, what we observe ordinarily is the contrary: indicator-​based processes have become universally embraced as scientific, facilitating comparisons between different objects and subjects. Numbers as quantitative classifications carry scientific connotations of 2

Introduction

precision, methodological rigour and neutrality, but, in reality, there are many particularistic, qualitative choices and valuations underlying global indicators and related policy scripts. The absence of this acknowledgement is significant because it has implications for globally diffused policies designed to mitigate and transform our collective futures. For those interested in how national, regional and transnational administration function, and how policies across the knowledge sectors are isomorphic, understanding the ways in which knowledge alchemy works is fundamental. We are all enchanted by the ‘magic’ of explaining social phenomena and predicting futures. Max Weber referred to disenchantment (Entzauberung) related to scientific analysis and rationalization that replaced religious explanations and effectively took its social role (Weber 2016). While, for Weber, rationalization gradually took the place of religious beliefs, we can, on the contrary, observe the development of new forms of worldly enchantment linked to ‘scientific’ global governance. Robert K. Merton –​a prominent figure of modern sociology whose analyses of the work of scientists remain an inspiration to us –​actually had been a young aspiring amateur magician, who even ended up using his stage name in his future profession and adult life (Calhoun 2003). In his research, he was particularly sensitive to the social mechanisms by which values such as academic excellence are socially produced. Inspired by Arsène Houssaye’s term ‘the 41st chair’ (Houssaye 1864; Merton 1968, 2), he used the example of the 40 ‘immortals’ of the Académie française to illustrate the qualitative differences between the chosen ‘immortals’ and the 41st, a ‘chair’ reserved for a mere mortal, and often unlucky, candidate. Following a standard definition (cf Plato 2008), we define knowledge as a belief that is considered and justified as being true. Justification means that the belief comes with an account. We need to add to this definition, for the time being. This addition emphasizes the temporal character of knowledge. For instance, we know today more about the COVID-​19 pandemic than when it first started. We are not interested in the epistemology of knowledge as such, but rather on how knowledge or what is believed to be knowledge is produced and justified or legitimized in concrete circumstances and how it impacts definitions of reality and the distribution of power. Comprehending how knowledge alchemy is pervasive in today’s governance is crucial for understanding how contemporary transnational administration functions and how global policy is developed, implemented and assessed. Using examples from North America, Europe, Asia, and international and transnational organizations that produce, broker and use comparative assessments, models and data on knowledge governance, we attend to the ideas, actors and practices involved in knowledge alchemy, as well as the outcomes. This chapter sets the foundation and introduces the analytical tools for this discussion. We show how the formulation of global models in 3

Knowledge Alchemy

innovation and higher education using indicators and algorithms and related conventions of knowledge production is guided by numerical scripts and formulas of global competition.

Conjuncture of ranking and digitalization The rapid growth of global indicators also promoted certain policy models for knowledge governance. Standing research assesses rankings and indicators as tools of global governance (Cooley and Snyder 2015; Anheier et al 2018), sources of power through quantification (Davis et al 2015; Merry et al 2015) and as governmental knowledge resources (Rottenburg et al 2015). Scholars have discussed the changes on the higher education landscape concerning global university rankings, focusing on the methodology of rankings (Dehon et al 2009; Shin et al 2011) as well as their effects on higher education policy and institutions (Kehm and Stensaker 2009; Hazelkorn 2011; Erkkilä 2013). Previous research has highlighted the discourse and global model of ‘world-​class university’ that now structures national higher education policy (Shin and Kehm 2012; Mittelman 2017; Rider et al 2020). There are also studies on global governance that identify university rankings and education as one of its elements (King 2010; Chou et al 2016), or analyse how field development of ranking in competitiveness, good governance, higher education and innovation now constitutes a global model of knowledge governance (Erkkilä and Piironen 2018). At the same time, artificial intelligence (AI) has become a global policy issue, linked with major social challenges as well as promises of unforeseen economic potential, productivity and enhanced well-​being (Brynjolfsson and Mcafee 2012; Mcafee and Brynjolfsson 2017; Feijóo et al 2020; Pencheva et al 2020). Most of the analysis on the social consequences of algorithms and artificial intelligence broadly explore the potential changes in working life (Brynjolfsson and Mcafee 2012; 2016; Ford 2016), professions (Susskind and Susskind 2017) and related corporate strategies (Mcafee and Brynjolfsson 2017) that are now also echoed by international knowledge brokers and ranking producers such as the World Economic Forum (WEF) (Schwab 2017). There are also critical observations about the ‘AI revolution’ concerning qualitative shifts in capitalism and evolving power relations that are seen to pose significant, even existential, challenges to democracy and economy (Zuboff 2019; O’Donovan 2020), involving accountability issues, systemic inequalities, and biases of algorithms (Barth and Arnold 1999; Pasquale 2016; Bucher 2018; Eubanks 2018). Principally, regulation of AI is approached as an ethical issue (Council of Europe 2018; Wong 2020; Ulnicane et al 2021). In practice, it has become a matter of competition policy (Murgia and Khan 2019; Murphy and Waters 2019; Stacey 2019). 4

Introduction

Digitalization and automation are seen to challenge countries’ capacities in knowledge production and human capital. Education and innovation have become central for national economic competitiveness, marking a paradigm shift in industrial policy (Ketels 2006; Aiginger and Vogel 2015; Aiginger and Rodrik 2020), also visible in the global models and metrics of competitiveness. Our analysis takes this conjuncture (cf Mahoney 2000) of global ranking and digitization as its starting point. It holds that global rankings increasingly constitute a policy agenda on competitiveness amid digitization, associating it with human capital and competition over ‘talent’ and research ‘excellence’. We see such policy models narrowing out policy alternatives and analyse critically the process where they are being adopted and upheld. We are particularly interested in the attempts to identify success formulas on institutional forms and practices that are critical for economic competitiveness. We further see these now increasingly relating to education that bridges innovation and global competitiveness, but also shifts the focus on cities that effectively host the innovation hubs and universities (Erkkilä and Piironen 2020a). In addition to fostering local innovation and knowledge creation, the global policy models also highlight talent mobility, that is, attracting talent through migration (Chou 2021). Countries, businesses and institutions all want talent, but how talent is defined remains contested. According to Cerna and Chou, talent has been defined in at least two ways in the management and migration literatures (Cerna and Chou 2019). First, talent can refer to certain qualities that an individual possesses, and thus a talent can be identified through the presence or absence of such qualities. These qualities can be intrinsic (attributes an individual was born with) or extrinsic (attributes an individual acquired). Second, talent can be used as a relational concept, in which the addition of an individual improves the overall performance of an organization or business. Here, talents are identified through their demonstrated behaviour in comparison to other individuals in the same organizational setting or within distinct time settings. For Cerna and Chou (2019), how talent is defined has implications for policy design (see Chapters 3 and 5). The policies on competitiveness and human capital are actively influenced by international actors such as the World Bank and WEF. There is a recent ‘digital turn’ in the work of these organizations that is directly linked with their ambit to measure countries’ economic competitiveness and innovation amid the emergence of AI and automation, often termed the ‘fourth industrial revolution’. This is visible in the revision of the existing measures, now claiming to analyse our digital future (World Economic Forum 2019c; INSEAD et al 2020), but there are also new signatory governance indicators launched to rate and predict the winners and losers of the AI revolution (Tortoise 2019a; World Bank 2019). 5

Knowledge Alchemy

In previous accounts the general development in global ranking and its relation to algorithmic reasoning has been acknowledged (Johns 2016), but not systematically analysed.1 Our book provides the theoretical tools for understanding how the global field of ranking has developed and why, and where it is going as it is increasingly linked to ideas of automation (see Chapters 2 and 3). Instead of a single policy domain, we focus on knowledge governance and see the rankings of universities, innovation and competitiveness as interlinked, feeding into global policy models and imaginaries that now assume major changes in demands for knowledge and human capital due to the rise of algorithmic reasoning. Furthermore, while there is consensus that indicators influence the policies of nations, there are few if any coherent analyses of what precisely makes them influential. Our book offers the theoretical structures with empirical backing to assess the institutional effects of numerical governance in the context of competitiveness and innovation, now increasingly linked to higher education, human capital and talent competition. This book is also concerned with debates on policy diffusion and convergence, particularly from the perspectives of policy scripts, rankings, narratives and conventions of knowledge production.

Policy diffusion and convergence Scholarship on transnational governance has focused on global models and policy scripts as blueprints for policy reform (Carruthers and Halliday 2006; Drori et al 2009; Halliday et al 2010; Pinheiro and Hauge 2014), associated with debates on policy diffusion (Radaelli 2000; Simmons et al 2006) and adoption and translation of global policy ideas (Sahlin and Wedlin 2008; Gornitzka 2013; Alasuutari et al 2015). The so-​called ‘World Society’ theorizing (Meyer et al 1997) ties these developments to rationalization of organizational forms that progresses with the global expansion of science, education and individual rights (Ramirez et al 1997; Schofer and Meyer 2005; Koo and Ramirez 2009; Beck et al 2012). Scholars have also drawn attention to the role of international organizations in writing global scripts (Meyer et al 1997; Finnemore 2014; Kentikelenis and Seabrooke 2017). The notion of soft power, associated with the cultural institutions and attempts to promote them through public diplomacy, highlights popularity and attraction as mechanisms of influence

1

Previous analysis on automation and algorithmic governance often provide broad overviews of the promises and perils of AI (Armstrong 2014; Shanahan 2015; Bostrom 2016; Domingos 2017; Tegram 2017) that go beyond the transformations related to more traditional government information systems (Dunleavy et al 2006; Hood and Margetts 2007; Milakovich 2012; Falk et al 2016). 6

Introduction

of dominant nations that go beyond outright coercion (Nye 2004; 2008). The use of soft power in diplomacy has garnered interest from higher education observers, resulting in the emergence of an area of studies on knowledge diplomacy. To be sure, international organizations such as the WEF have also enjoyed such allure and used it in influencing global policy agenda (Garsten and Sörbom 2018). This also links to the global spread of liberalism (Simmons et al 2006) and the liberal world order (Börzel and Zürn 2020). Critical scholarship has also discussed this as hegemonic ideas of capitalism and its relocations, entailing also discursive power (Sum 2009; Sum and Jessop 2013). However, though previous research readily associates global indicator knowledge as instruments of promoting liberalism, illiberal regimes can also play the ranking game. China is actively promoting its science and education system with the help of numerical comparisons (Candido et al 2020) and has become a powerful actor in university alliance building (see Chapter 6). There are also other ‘numerical superpowers’ that punch above their weight in the assessments, legitimizing their systems of governance. Often termed as ‘outliers’ in the ranking analysis, the Asian Tigers such as Singapore and Hong Kong are shining in the comparative assessments, despite being criticized for their system of governance. This also shows limitations in the ways the indicators are laid out. Previous assessments have highlighted numbers and indicators as policy instruments, though their effects carry the potential for unintended consequences (Smith 1995; Thiel and Leeuw 2002; Robinson 2003; Pidd 2005; Espeland and Sauder 2007). Comparing and identifying countries and cities as forerunners and laggards in innovation and digitization, the global rankings also obtain governing functions, challenging traditional analysis of international relations (Löwenheim 2008; Porter 2012; Broome and Quirk 2015; Kelley and Simmons 2015). Scholars often refer to global indicators as Foucauldian technologies of discipline (Löwenheim 2008; Broome and Quirk 2015) or means of instrumental rationality in a Weberian sense (Erkkilä and Piironen 2009), but also more subtly as tools of ‘social pressure’ (Kelley and Simmons 2015), ‘reactivity’ (Espeland and Sauder 2007) and ‘quiet power’ (Merry et al 2015). Scholars of higher education and innovation have pointed out how rankings have had profound effects on global higher education (Kehm and Stensaker 2009; Shin and Kehm 2012; Mittelman 2017; Rider et al 2020). The global models are being mimicked also by those who are not credibly capable of becoming world-​class institutions or even being ranked (Kamola 2014a; Erkkilä and Piironen 2020a). Countries are in different positions in ‘channelling, filtering and buffering’ the effects of global policy prescriptions due to their size, centre–​periphery positioning and institutional traditions (Gornitzka 2013), though governance by numbers is also prone to generate 7

Knowledge Alchemy

paradoxical and unanticipated outcomes (Espeland and Sauder 2007). For example, in peripheries of global innovation and knowledge creation, the attempts to embrace global scripts might even reinforce peripheralization and further exclusion from global innovation (Münch and Schäfer 2014; Hanafi and Arvanitis 2015; Cantini 2019; Yarrow 2022). Such ‘global’ policy scripts easily overlook ‘local knowledge’ (Scott 1998) and ‘epistemologies of the south’ (Santos 2014; 2018) or the local context (Guzmán-​Valenzuela and Barnett 2013). The local application of a global model and its limits is often discussed as ‘decoupling’ or ‘loose coupling’ (Ramirez 2012), ‘glocalization’ (Drori et al 2014), ‘translation’ (Pinheiro and Hauge 2014) or ‘domestication’ and ‘synchronization’ (Alasuutari 2015) of ideas. These concepts mainly focus on the adoption of global models and inclusion in epistemic governance but tend to underplay agency and autonomy by those adhering to global norms and scripts (cf Perrotta and Alonso 2020), let alone institutional and political conflicts and active contestation of such models. While previous research on policy indicators highlights their effects on national policies, we in addition draw attention to the conventions of numerical knowledge production that are currently steering global policy scripts of innovation and competitiveness. For those interested in knowing how global policy is formulated, implemented and assessed, understanding how knowledge alchemy triggers which mechanisms to produce legitimate evidence is thus an important step towards comprehension.

Knowledge alchemy and conventional power It is widely held that modern individuals are rational, or at least this is the picture that is often painted of them in many political science textbooks. Economic theory and rational choice theory have elevated this principle into a doctrinal cornerstone. One effect of this belief is that events and actions that are in contradiction with this belief are forgotten or brushed under the rug. While it is easy to show that primitive man held all kinds of irrational, magical beliefs, it is more difficult to do that with modern man in what Freud already called our ‘scientific age’ (Freud 1985). It is nevertheless evident that society and political life, higher education included, contain a lot of irrational and even magical aspects. One is the production of money, which is basically something that banks, private and public, perform by waving a magic wand (Boyer 2020), that is typing a sum on a keyboard and saving it on a spreadsheet. They succeed in doing what alchemists had been unsuccessfully trying for a long time, transforming nothing into something valuable. But alchemy does not only concern the financial world. The transmutation of value and the alchemy of knowledge that goes with it are ubiquitous in society. 8

Introduction

For students of human rationality like Sir James Frazer and Freud, similarity and contiguity were the two principles of the oldest form of human knowledge, magic and animist thinking. To quote Frazer, ‘(M)en mistook the order of their ideas for the order of nature, and hence imagined that the control which they have, or seem to have, over their thoughts, permitted them to have a corresponding control over things’ (Frazer 1920, 420). A natural phenomenon could be ‘produced’ by simulating it through human action. The rain dance is a good example of this performative process. Similarly, by simulating a form of human organization such as a university, university administrators believe the same effects are produced. Isomorphism in organizational studies is a variant of this similarity thinking. In contrast to similarity, contiguity refers to the simultaneous ‘presence’ or the association of certain values or actions. For instance, academic excellence requires the ‘presence’ of certain objects such as a large endowment, for instance, or actions such as winning prizes that can be found in valued academic institutions. Similar magical processes are at work when social values such as academic excellence are created. For instance, by instituting a categorical dividing line between those who enter university and those who drop out, a line differentiating the excellent from the ordinary is created from nothing. As we mentioned earlier, Arsène Houssaye’s book on the 41st seat of the Académie française provides a great example of this kind of social differentiation. Considered the most famous citizens in France, the 40 ‘Immortals’ were elected for life. Houssaye’s term of the ‘41st seat’ referred to those who were not elected. In his book, Houssaye derided the election of unknowns and the growing list of luminaries, the likes of Descartes, Pascal and Rousseau, who never made it but deserved a seat among the ‘Immortals’ (Houssaye 1864, 30). Compared to the ‘Immortals’, a regular mortal occupied the 41st seat. But was it the fault of Descartes or Pascal, asked Houssaye? Or was it the fault of the Académie? How do we explain this seemingly contradictory outcome? The same qualitative process is at work when global ranking lists qualify a university as a world-​class university. In the case of ranking, quality becomes an emergent property of quantity. These categorizations have in common the qualitative transformation of something banal into something exceptional by separating qualitatively two groups from one another, producing value by instituting an ideal, invisible dividing line that is taken as a real divide between the worthless and the valuable. Objects grouped are then attributed certain values by the mere act of instituting a dividing line. Real likeness between them is secondary. Contact with the valuable also highlights the contagiousness of social values. For instance, this can be seen in the hosting of prestigious visiting professors or having faculty visiting or engaging in partnerships with those based at ‘top’ universities. 9

Knowledge Alchemy

In global knowledge governance, alchemy is at work in two analytical phases, digitization and digitalization (see Table 1.1). In the first phase, some knowledge is differentiated from other knowledge because it can be transformed into numerical data that can be instrumentally used in view of a goal. For instance, knowledge of incoming students can be quantified and digitized. The first phase consists in the transmutation of knowledge into numerical data that takes digital form (quantification and digitization). This phase is followed by a second alchemical operation, the transformation of data into economic utility (digitalization). In higher education, commodification might include a calculation of the pros and cons of raising the study fees for certain groups of foreign students. This second phase includes the possibility of automation, of defining levels for automatic action or strings of actions for instance. Efficiency will be calculated based on the data available and, for instance, the level of fees will be dependent on the number of incoming students. For those interested in knowing how transnational administration works, it is essential to grasp that digitalization is now part of its everyday work. The pervasiveness of knowledge alchemy cannot be ignored because these transmutations, often taking place in the background, have a significant impact on the distribution of power in knowledge governance. Value formation is massively externalized to private companies like Google, Amazon, Facebook (Meta) and Apple, and to knowledge brokers such as Clarivate Analytics (web of science) that produce the databases and the digital tools that are used in public decision-​ making and in the evaluation of their efficiency. In higher education, ranking tools and the numerical goals they help set are used to reform universities and evaluate the performance or excellence of individual academics and departments or subject areas; similarly, indexes of competitiveness impact and steer city strategies. What are the conditions for the creation of value from nothing? The first condition is that data must be useful for someone (the tyranny of convenience). Digital devices that succeed in creating plus value, for instance something that helps in some banal activity like going from home to the workplace, are potentially economically valuable. Apple succeeded in beating Nokia because it provided more convenient features not sophisticated features. If a ranking list passes the convenience test, that is, it makes it easier for someone (such as a student, an administrator, a politician) to evaluate Table 1.1: The two phases of knowledge alchemy Digitization

Digitalization

Information → Knowledge → Data

Data → Economic/​non-​economic capital

Source: Kauppi (2020)

10

Introduction

the quality of an institution of higher education, then this individual will consider this tool as being legitimate. The proof is in the pudding. A second answer is that value creation is based on the belief in the legitimacy of some social carriers of the actions in question. To succeed, it involves agents as tool users who are believable, credible and thus legitimate. When your bank tells you that your loan request has been accepted, you believe that you will receive the money. The same could be true of entry exams instituted by universities, ranking lists of universities produced by ranking agencies, indicators of competitiveness created by important economic players like the WEF and so on. The reflex or non-​cognitive initial reaction is to trust, until further notice. If the university in the top group is not excellent and you can prove it, or you consider the metric as being bogus, then the magic might not work for you. But as long as the transmutation is not put into question by a relatively large group of people, it works. This is what the power of social convention is about. But this peer pressure has evolved with dramatic technological changes that have created new forms of social domination. The term ‘conventional power 2.0’ (convention +​data) (Kauppi 2020) describes the special kind of symbolic domination in global higher education. The sociology of conventions teaches us that a convention provides the implicit, taken-​for-​g ranted background that conditions action. For it to work, it is not questioned. For this reason, it is difficult to resist it: there is no clear target, not even the convention itself as it is sufficient for domination to work when users are assumed to be familiar with it. In global higher education, rather than materially, economically or legally forced to adapt to the new rules, the users of university rankings ‘freely’ adopt their values. Used to academic hyper-​competition, for them performing on a standard-​based global level is psychologically rewarding and socially compelling (Graz 2019). Even more so as the values promoted are now represented in ‘scientific’ and ‘objective’ form as data and digital tools, based on their users’ implicit trust in numbers (‘number hypnosis’), a second key ingredient of conventional power 2.0. This hybridity of soft and hard power in conventional power 2.0 exemplifies a modern version of Max Weber’s ‘steel-​hard casing’ (Weber 2010). The more these tools are used, and their use institutionalized and embodied in institutional and personal self-​identity (Kauppi 2020), the harder the casing gets and the more difficult it will be for their users to free themselves. Conventional power 2.0 combines the silent force of the imaginary produced by the convention (including implicit peer pressure and the fear of not being finally included in the ‘in-​group’ as a ‘world-​class university’) and the brute force of data (as ‘evidence’). Knowledge alchemy as an increase in the value of an object is a core technique of global knowledge governance in the neoliberal age. Knowledge devaluation is the symmetrically opposite transformative technique whereby some type of knowledge is devalued or just simply ignored. For knowledge 11

Knowledge Alchemy

alchemy to work, knowledge devaluation is necessary. For instance, the use of the teacher/​student ratio as a numerical representation of teaching quality requires that some other types of representations of teaching quality are devalued or ignored. In contrast to these transformative techniques a third ubiquitous core technique that is used in the second stage of knowledge alchemy is reproduction, that is that data or parts of it are copy-​pasted to value or devalue certain objects. As we will see later, certain types of knowledge, such as those produced by leading higher education institutions like the Institut Européen d’Administration des Affaires (INSEAD), are more prone to reproduction than other types of less legitimate knowledge.

Policy scripts, rankings and conventions of knowledge production Policy scripts are generally understood as generic policy models that define specific but generalizable measures to address a policy issue, while prescribing action (Kentikelenis and Seabrooke 2017). The generalizability and predetermination of global policy scripts, as well as their prescriptive nature, has intensified through the use of indicators, as we discuss in Chapters 2 and 3. Global policy models on economic competitiveness, innovation and human capital are increasingly communicated with the help of global indicators; relevant rankings are known by everyone in a policy field and allow comparisons and shared understanding of goals. While the figures are intended as tools of evaluation, they also have constitutive effects (Kauppi and Erkkilä 2011; Dahler-​Larsen 2014), influencing countries’ innovation governance through their reflexivity over rankings. One constitutive aspect of numerical governance are the path dependencies of indicator production. We see this as field development in global ranking (Kauppi and Erkkilä 2011; Erkkilä and Piironen 2018; 2020a), where new indicators are emerging to challenge the existing ones with methodological improvements or new conceptual avenues. This classification struggle is also linked to competition between the international organizations and other index producers that are under pressure to develop their signatory indicators as they compete with each other over prestige and funding (Freistein 2016). But this race to produce alternative metrics has consequences for global governance, as the knowledge production has entered an ontological trap: to challenge the simplified policy feed of existing rankings, the knowledge producers are compelled to produce alternative figures (Erkkilä and Kauppi 2010). You can only fight the numbers by creating yet another number. Typical for structuration is that the attempts to create novel institutional practices often end up reproducing existing ones (Giddens 1984, 5). This is apparent in the work of the aspiring knowledge producers who are under pressure to validate their figures against the existing metrics (cf Gieryn 12

Introduction

1983; Haas 1992), leading to conformity through the sharing of concepts, methods and even data. Moreover, most of the global rankings are so-​called composite indicators that draw their data from various sources. Hence, the production of such rankings is conditioned by the availability of data. As we will discuss in our empirical analysis (see Chapters 2 and 3), the field development in global ranking now proceeds through the availability of data, steering the potential topics of analysis as well as its conceptual operationalization, and not vice versa (cf Sartori 1970). We argue that the conventions of making, validating and using numerical knowledge now constitute global policy models of competitiveness and innovation, making them also seemingly transferable from one context to another. The simplified numerical representation of complex policy issues allows the models to travel, but, more importantly, the numbers now also allow holistic analysis through merging of different datasets, which leads to the intensification of policy scripts that draw elements from different policy domains, while being monitored almost real-​time. The governance of knowledge and human capital is a prime example, as it is currently linked to policies of innovation, migration, urban governance and individual rights. These associations are constituted already on the level of indicator production, through reuse and borrowing of data, and its algorithmic manipulation.

Time, narratives and conventions However, to become effective, the numerical knowledge also needs to be narrated and communicated. As knowledge structures, scripts describe predetermined sequences of events based on storylines (Schank and Abelson 1977). Our analysis of policy narratives shows how claims about the future are done through references to the past, as various ‘traditions’ are evoked to justify policy measures. Such invented traditions (Hobsbawm 1987) also coincide with conceptual shifts that happen in a context where past concepts of governance no longer fully fit the horizon of expectation of the anticipated future (Koselleck 2004). Political scientists have examined and conceptualized ‘time’ in politics and policy making from various perspectives. We can understand time as a form of power, as a structure conditioning development, as a category of continuity and change, but also as horizon of expectation that is tightly linked with conventions and practices. Time is also present in discussions of Zeitgeist or ideational cycles (Marcussen 2000), entailing ideological trends. Studies on institutional and political traditions as well as organizational memory and culture also hold time as an element of analysis (Walsh and Ungson 1991). Time is perhaps most clearly present in institutional analysis, where institutional development and structuration is discussed under path 13

Knowledge Alchemy

dependence (Pierson 2000; 2004), and sequencing and conjuncture of institutional developments (Mahoney 2000). Time is also implied in the analysis of incremental institutional evolution (Thelen 2004) and (rapid) changes through crises and junctures of governance (Krasner 1984; Peters et al 2005) that result in transformation. The tempo of time has fascinated scholars of higher education interested in the transformation of modern academe. For instance, higher education scholars have studied time as a category of change in ‘accelerating academia’ (Vostal 2016) and creating and managing future horizons in ‘anticipatory governance’ (Robertson 2017; 2022). Since present judgements are often said to be based on earlier decisions, ideational schemas also tend to steer and captivate ways of doing things, causing ideational patterns (Marcussen 2000, 17). The ideational route that we choose to take over time creates a pattern of its own, because actors validate their decisions at present against those made in the past. This logic comes close to that of historical institutionalism and path dependence, as well as Giddens’ view of structuration, where attempts to create something new often tend to reproduce existing practices (Giddens 1984), a process of reinforcement. In a similar fashion, scholars of science and technology studies have analysed ‘socio-​technical imaginaries’ that carry narratives and ideas of collective technological futures (Jasanoff and Kim 2015). This also links to assessment of time in analysis of political imaginaries that build on shared cultural ethos and cognitive structures (Castoriadis 1987; Anderson 1991; Taylor 2002; 2004; Strauss 2006). Scholars of political economy have highlighted the importance of imaginaries for capitalism (Jessop 2004; 2010), particularly in assessments of uncertain future (Beckert 2016). Imaginaries are also highlighted in analysis of globalization and global governance (Archer 2012; Steger and James 2013; James and Steger 2014; Kamola 2014a; 2014b; Alasuutari and Qadir 2016). We see the rankings constructing an imaginary of global competition, where countries, regions and cities are out to compete with their like units. Moreover, the global indicators colonize the future (cf Robertson 2017) through their rigid assessment criteria, predictions based on countries’ past performance, and by framing education policy as a matter of economic competitiveness. Imaginaries can also be considered as being forms of ideational interests that exist in specific relationships with material interests. While for Marx, material and more specifically economic interests ruled the world, for Weber, contrary to many misleading interpretations, ideational and material interests were in constant interaction with one another (Weber 1959). His famous train track metaphor captures some of these dynamics. While material interests determine the direction and speed of the train, in some historical circumstances ideational interests, for him developments in religion, can redirect the train and redefine social development. A religious outlook like Calvinism can have a decisive impact on economic development. 14

Introduction

Imaginaries are not only linked to material and ideational interests but also to habitual patterns of behaviour that in turn determine what has priority. For instance, ways in which actors classify the world around them, or the cultural conventions that determine what is normal and legitimately expected (Duby 1980). These social and cultural structures vary with time and space. Ideational schemas usually change in a process where they are delegitimized or disconfirmed. This usually happens after a shock/​punctuated equilibrium or critical juncture, when there is an ideational vacuum or multiple crises (Marcussen 2000; Blyth 2002; Schmidt 2002). However, to become embedded in the existing institutional practices new ideas and policy discourses also need to resonate with the prevailing public values, narratives and collective memory (Somers and Block 2005), often related to references on institutional traditions (Neustadt and May 1986; Hobsbawm 1987). In fact, such reflexivity over institutional history and traditions is a mechanism of policy diffusion (Erkkilä 2012). We can also criticize institutional analysis for their somewhat generic analysis on the role of culture as an explanatory factor for institutional development. Culture is often reduced to values and norms, beliefs, cognitive and ontological models or scripts, without elaborating the mechanisms through which these become effective (DiMaggio and Powell 1983; Meyer et al 1997, 149–​151; Mahoney 2000, 517; Schmidt 2006, 107). We would wish to draw attention to conventions in upholding institutions, as well as habituation as a mechanism through which conventions are embedded into institutional practices. Such conventional power is not rooted in active reflexivity over norms, as conventions are regarded as legitimate and self-​evident, pervading, hence mundane, practices of everyday life. It also further broadens the horizon for structurally inclined institutional analysis by asserting individuals in the analytical frame. For example, instead of simply considering the self-​ reinforcing processes of path dependence from the perspectives of economic constellations or normative pressures, we should in addition consider its mundane habitual aspects where practices are not actively reflected upon but rather conducted ‘as usual’ by individuals. There are also considerations of time in social science research methodology (Abbott 1997; 2001; Mahoney 2003; Vennesson 2008; Neale et al 2012). Conceptual historians have observed ideation changes over time, drawing attention to conceptual change in both accommodating ideational change and driving it (Skinner 1969; 1989; Koselleck 2004). Reinhardt Koselleck (2004) has also drawn attention to different timescapes, such as slow and fast time and their synchronization. Our analysis of policy narratives shows how claims about future are done through references to the past, as various ‘traditions’ are evoked to justify policy measures. Such invented traditions (Hobsbawm 1987) also coincide 15

Knowledge Alchemy

with conceptual shifts that happen in a context where past concepts of governance no longer fully fit the horizon of expectation of the anticipated future (Koselleck 2004). Our analysis of the narrative elements of global policy models explores invented traditions in knowledge governance and mobility. We show how the transnational policy scripts now build on historical narratives of pre-​modern-​era city-​states (Medici-​era Florence), Popperian open society and early 20th-​century Vienna, as well as the myth of the medieval scholar. There are also economic and technological narratives seeking historical reference points in industrial revolutions and innovations of the past. These narratives now argue for continuity of governance at times of disruption and propose seeming solutions for current challenges of knowledge governance.

Conclusions To summarize, our book explores the interrelation of global indicators, algorithms and knowledge governance. We aim to provide detailed and critical analysis of the rationalities and mechanisms of global numerical governance and diffusing policy models. The theoretical and conceptual frame followed throughout the book explores the emerging conventions and practices that build on predetermined formulas, models or scripts. We refer to this process of transmutation as knowledge alchemy. The book also provides detailed case analysis and a narrative on the development of numerical governance and its central actors (the global knowledge producers, brokers and users), as well as the productivist bias (quantity over quality) and imaginaries (as ideals and embodied action) (Kauppi 2000; Strand and Lizardo 2015) with which they operate. Acknowledging historical aspects of governance, narratives and culture as well as institutional trajectories in absorbing and shaping the global policy scripts, we further assess how the models are being translated in national, subnational and institutional contexts. The book is organized in two parts. In Part I (Chapters 2 and 3), we detail the making of the models of global knowledge governance over time, focusing on the indicators proposed to assess competitiveness, introducing the network of indicator producers and brokers, and discussing the increasingly shared datasets used to construct such rankings. Here, we describe how knowledge alchemy became the dominant mechanism in global knowledge governance in the last decades. In Part II (Chapters 4, 5 and 6), we turn to the scripts or storylines, and the many sectoral policies and initiatives generated through knowledge alchemy. Specifically, we analyse how the imaginary of the global talent competition drives much of what we see today in global knowledge governance. Our analysis is a critical one. We highlight how the narrative of the global talent competition stems from selective interpretations of the historical past (in particular, the lore of the 16

Introduction

medieval scholar) that is now projected on the current understanding of the world, and how to organize our future. In the concluding chapter, we step back and reflect on what knowledge alchemy holds for our collective future and argue for the need to regain reflexivity as we are hurled down the digital train track, made possible by the immense data collected about us and purportedly for us.

17

PART I

Indicators, Data and Models of Global Knowledge Governance

2

Global Rankings of Good Governance and Higher Education Introduction This chapter provides an overview of the development of global rankings in good governance and higher education. This also serves as a background for Chapter 3, where the metrics in city competition, talent and AI-​ readiness are discussed at the cusp of automation. Indicators and rankings are outputs of algorithmic reasoning; often, the aggregate figures that allow rank orders are based on statistical operations according to a predetermined logical order. These two chapters explore some of the taken-​for-​granted presuppositions of data-​driven knowledge governance. In so doing, we aim to show the convergence and reinforcement of a global imaginary, norms and anticipations produced and sustained by elite networks in public and private institutions. Initially, the metrics dealt with good governance and competitiveness of countries, but since the 2000s the global rankings on higher education and innovation have emerged. Recently, city rankings have highlighted the importance of assessment of academic research and education. The effects of these rankings have been numerous, and innovation, higher education and academic life more generally have been increasingly governed by high-​pace data-​driven reforms, as for example our discussion on the case of Paris-​Saclay University demonstrates. The indicators shape perceptions about national and regional ‘models’ and learning from others. Analysing global indicators on education and innovation, we discuss the kinds of political value choices made in the production and use of data, and the ways in which quality is translated into quantity. As will become commonplace in our subsequent chapters, knowledge alchemy translates the formula of ‘quality into quantity’ magically into ‘quantity means quality’ in a variety of policy domains (see Chapter 6). We also explore the field development of global ranking as a set of practices 21

Knowledge Alchemy

and values that cut across established policy areas, where new actors are entering the field with alternative measurements. Though rapidly increasing in numbers, global indicators overlap ideationally, methodologically and most of all epistemologically by the sharing of the same data. This high degree of concentration of data means that, through a selection process, certain datasets and the institutions that produce them take a central and outsized role in global governance. The existing data sources are now used in composite indicators, most notably the innovation rankings, that incorporate the available, often same, data sources and manipulate them algorithmically to produce new and seemingly alternative indicators. We will begin with a discussion of the field development of global ranking, showing how global university rankings emerged in the wake of rankings on competitiveness and good governance. We will then move on to discuss the ‘world-​class university’ model and shifting strategies in higher education.

Field development of global ranking Since the mid-​1990s there has been surge in the number of global indicators measuring various aspects of national policies and institutions. The rapid expansion of global indicators can be understood as a development of a global field of ranking (Kauppi and Erkkilä 2011; Erkkilä and Piironen 2018), where new actors entering the field have to conform to the existing norms and rites of verification (Haas 1992), while also trying to carve out their own niche (Gieryn 1983). Table 2.1 shows how the global ranking field has evolved with selected indicators in competitiveness, governance, higher education, and human capital and innovation emerging over time. Altogether, the number of indicators has grown in all the policy fields, while they have also become geographically and thematically more nuanced. The first rankings of good governance and competitiveness were published in the 1970s and 1980s, but the actual drive for global comparative assessment began in the late 1990s and early 2000s with the World Bank’s Worldwide Governance Indicators and Transparency International’s Corruption Perception Index paving the way for other datasets. Both of these are subjective, perceptions-​based measures of governance (see Kaufmann et al 2010, 18). Scholars have identified different generations of indicators (Knack et al 2003). The first generation of indicators were often composite indicators, such as the Worldwide Governance Indicators, which drew data sources from various other datasets. Such indicators were often rankings that aimed to form a list of performing countries or institutions. Recently, there has been a shift in the way governance is assessed globally, as more nuanced and detailed numerical assessments, often referred to as second-​generation or actionable indicators, are challenging established 22

newgenrtpdf

Table 2.1: Global indicators of competitiveness, governance, higher education, human capital and innovation 2010–​

Competitiveness

Global Competitiveness Report (1979) World Competitiveness Yearbook (1989)

Global Business Competitiveness Index (2000) Growth Competitiveness Index (2000) Global Competitiveness Index (2004) Worldwide Centers of Commerce Index (2007) Rich States, Poor States (2007) A.T. Kearney’s Global Cities (2008) Global Power City Index (2008)

EU Regional Competitiveness Index (2010) Hot Spots 2025 (2013) The Competitiveness of Cities (2014)

Governance

Freedom in the World (1972) Freedom of the Press (1980) Corruption Perception Index (1995) Worldwide Governance Indicators (1996)

Fringe Special (2001) Press Freedom Index (2002) UN E-​Government Readiness/​Development Index (2003) UN E-​Participation Index (2003) Global Integrity Report/​Index (2006) Open Budget Index (2006) Open Net Initiative (2007) Actionable Governance Indicators (2008) Government at a Glance (2009)

Global Integrity Report –​Integrity Scorecard (2010) Global Right to Information Rating (2011) Implementation Assessment Tool (2011) Government AI Readiness Index (2017) Digital Government Index (2019)

Academic Ranking of World Universities (2003) Times Higher Education Supplement (2004) Webometrics Ranking of World Universities (2004) Affordability and Accessibility Comparison of Global Higher Education Rankings (2005) Performance Ranking of Scientific Papers for World Universities (2007) The Leiden Ranking (2008) The SCImago Institutions Ranking (2009)

QS World University Ranking (2010) Times Higher Education –​Thomson Reuters (2010) High Impact Universities (2010) The U-​Multirank (2011) The Assessment of Higher Education Learning Outcomes (AHELO) (2012)

Higher education

(continued)

Global Rankings

2000–​2009

23

Pre-​2000

newgenrtpdf

Table 2.1: Global indicators of competitiveness, governance, higher education, human capital and innovation (continued) Pre-​2000 Human capital and Human Development innovation Index (1990)

2010–​

European Innovation Scoreboard (2001) Global Innovation 1000 (2005) Global Innovation Index (2007) Innovation Cities Index (2007) Innovation Union Scoreboard (2008) International Innovation Index (2009)

The Bloomberg Innovation Index (2011) The Startup Ecosystem Report (2012) Thomson Reuters Top 100 Global Innovators (2011) The Global Cleantech Innovation Index (2012) Global Talent Competitiveness Index (2014) Top 100 Innovative Universities (2015) Contributors and Detractors (2016) Top 25 Global Innovators –​Government (2016) Human Capital Index (2018)

Knowledge Alchemy

24

2000–​2009

Global Rankings

rankings (Knack et al 2003; Trapnell 2011).1 The second-​generation indicators are also often referred to as ‘mappings’, as they allow different representations of data, instead of just a single aggregate number (that is, ranking). As non-​aggregated measurements the actionable indicators aim explicitly for causality –​for establishing a link between the use of indicators and the subsequent actions. Index-​producers have also called these new types of indicators ‘actionable’ governance indicators because they –​unlike rankings –​allow close monitoring and development of specific aspects of governance, providing guidance on reforms (World Bank 2009; Trapnell 2011). We understand this shift in the production of governance indicators as field structuration, where new actors join the field of global governance assessments with competing sets of indicators (Kauppi and Erkkilä 2011). In attempting to secure a position in the field, the actors engage in the production of competing classifications of reality. Such classification struggles also entail political conflict, particularly in the context of economic development. But for political conflict to manifest, it first must be made visible. Palonen (2003) identifies ‘politicization’ as a process where the political character of some phenomena or process is detected. This opens a new horizon for action as the previous status quo is challenged, making the issue a subject of renegotiation. Numbers are seemingly neutral and their political character often remains tacit (Porter 1996; Desrosières 1998). Their politicization is therefore important in the evolution of governance measurements, as it opens an opportunity for change, allowing new actors and ideas to enter the field. As we will show, the shift towards actionable governance indicators was sparked by the politicization of country rankings. One characteristic of field structuration is the unintentional reproduction of existing practices (cf Giddens 1984). This reproduction or structural inertia can have unintended consequence for governance reform, as actors claiming to change existing practices come (often unconsciously) to replicate them (cf Baert 1991) and, ultimately, to reinforce the very practices they seek to reform. New actors wishing to join the activity of governance measurements need to legitimize their knowledge products according to the criteria set by the epistemic community existing in the field (Haas 1992). As a result,

1

According to Knack et al (2003), second-​generation indicators are characterized by four criteria: (1) transparency, meaning that they should be replicable, well-​documented and that the data sources are not politically controversial; (2) availability, meaning that the data has broad country coverage and continuity over time; (3) quality and accuracy, meaning consistency across countries and validity of measurements; and (4) specificity, meaning that indicators measure specific institutions or output and that exogenous factors do not unduly affect the measurements. 25

Knowledge Alchemy

new indicators are likely to conform to the existing normative and causal beliefs and criteria of validity. The indicators are also policy instruments, functioning as means for collecting information, but also as effective tools of government in trying to influence the outside world (Hood and Margetts 2007, 3). Hence, changes in their outlook have implications for their mechanisms of influence. With regards to ranking, we could understand the mechanism of influence as Foucauldian governmentality, where actors adhere to a perceived norm constructed by the rankings (Löwenheim 2008; Erkkilä and Piironen 2009). Such ‘government at a distance’ (Miller and Rose 1990), where the actors external to the governed body are creating norms and goal-​settings that come to steer activities, is particularly evident in the actionable governance indicators that explicitly aim to become effective tools of policy making. It does so by creating goals of governance that can be observed from a distance. This resembles the social and institutional practices of accounting and auditing (Hopwood and Miller 1994; Power 1999), which we now see in the new domain of global indicators. Since the beginning of the 2000s, global ranking has evolved the most on two registers, on the one hand good governance, innovation and competitiveness, and on the other hand university performance. The field development of rankings initially progressed within good governance and competitiveness, where many of the first data sets were aggregate figures, that is, rankings. This was followed by the actionable ‘second-​generation’ datasets in good governance (Erkkilä 2016). In parallel, the global university rankings emerged following the launch of the Academic Ranking of World Universities (ARWU), more commonly referred to as the Shanghai ranking, in 2003. But this did not happen in an ideational vacuum, and it is important to understand how the world-​class university model associates with the more general discourse of competitiveness and its artefacts (Sum 2009; Sum and Jessop 2013). We argue that the rise of university rankings in a global drive for numerical comparative assessment is best understood as field development. Following Giddens (1984), we see a reproduction of existing practices in the construction of numerical information on good governance and higher education. But more importantly, we also see a change in the nature of policy scripts that are associated with the figures and show how the prescriptive nature of global policy scripts has intensified through the use of indicators.

Rankings of good governance Global governance indicators differ from the previous social-​scientific attempts at comparing countries (Erkkilä and Piironen 2009). Whereas large country comparisons were previously done mostly by academics, the practice 26

Global Rankings

today is largely in the hands of international governmental organizations (World Bank, United Nations Development Programme, Organisation for Economic Co-​operation and Development [OECD]), non-​governmental organizations (NGOs) (such as Transparency International, Freedom House), private businesses (such as Standard & Poor’s), business schools (such as INSEAD) and linked associations (including the WEF, Bertelsmann Foundation). Some are engaged in administrative or economic development, others in profit-​making. Also, the subject of measurement has shifted. Whereas previous academic assessments centred on the notion of democracy, more recent country comparisons focus on good governance (Erkkilä and Piironen 2009, 130–​ 132). Many assessments root the notion of ‘good governance’ in market liberalist and efficiency-​seeking perceptions of institutions (Erkkilä and Piironen 2014) put forward by international organizations of economic development (cf Seppänen 2003; Drechsler 2004; Zanotti 2005). We can connect the rise of the governance indices with the global concern over good governance and corruption (Ivanov 2009).2 In coinciding with the general pressures for economic globalization, the concern over good governance led to the development of governance indicators. The first of its kind, and a model for many, was the World Bank Institute’s Worldwide Governance Indicators (WGI). While the World Bank has used its Country Policy and Institutional Assessment tool since the mid-​1970s for assessing the eligibility for funding, the WGI were developed as a tool for general assessment on governance globally. This initially targeted specific problems of global governance, such as corruption. In the interviews conducted for an earlier study (Erkkilä and Piironen 2014; Erkkilä 2016), the developers of the WGI stated that as several existing measurements of corruption and accountability were not always coherent in their results, they devised the WGI to neutralize this variance by creating an aggregate number of the available measurements. But the unanticipated effect of this technical solution was that it gave the figures high media visibility around the world. Aggregation allows for ranking nations based on their relative position on the various measurements and the league table format has drawn a fair amount of media attention on certain global measurements. At the same time, the rankings were criticized. Most notably, and perhaps unsurprisingly,

2

In the World Bank, the issue of corruption was put firmly on the agenda in the mid-​1990s with the General Director referring in a public speech to the ‘cancer of corruption’. The previously unspeakable word ‘corruption’ was now out in the open, paving the way for attempts at assessing good governance globally. Another issue placing governance so firmly on the agenda of the World Bank was the collapse of communism in Europe, leading to problems in building state capacity amid corruption and state capture in the former communist countries (for terminology, see Bagashka 2014). 27

Knowledge Alchemy

rankings have been controversial in countries that fare poorly in them, indicating the politicization of governance indicators. For example, the WGI has caused opposition within the World Bank and there have been mounting political pressures from funding-​receiving countries to abolish the rankings. In 2007, nine executive directors of the World Bank representing countries such as China, Russia, Mexico and Argentina expressed concerns about the WGI, arguing that the World Bank should not be producing such indices. One of the concerns was China’s low ranking in the voice and accountability component of the WGI (Guha and McGregor 2007; see also Harding 2013). The increasing critique and understanding of the limitations of rankings can be interpreted as the politicization of governance indicators and rankings. Numbers are seemingly neutral (Desrosières 1998), but their politicization creates a new realizable horizon of chances and opportunities for action (Palonen 2003, 181–​182). In the field of global governance indicators, the critique of ranking has led to attempts to readjust the methodology but also to redefine the goal of measurement. The most visible critique of the rankings has been methodological, sparking a lively debate with and among the developers (Kaufmann et al 2010; 2011; Thomas 2010). The criticism of the existing rankings –​WGI in particular –​has led to attempts to develop indicators that are more appropriate and methodologically advanced (McFerson 2009; Andrews et al 2010; Gramatikov et al 2011; Joshi 2011). Aggregation, the aim to make single ranking numbers, has drawn much attention to the first generation of governance indicators (Langbein and Knack 2010), as the discussion around the WGI indicates. The WGI have also been criticized for the use of aggregate figures by the OECD, which has been creating its own non-​ aggregated dataset. The development team of the WGI has also over time downplayed its optimism about aggregation and ultimately denounced ranking as a technique for comparing countries (Kaufmann et al 2008, 5; Erkkilä and Piironen 2014). Another methodological debate addresses the validity of the measurements and the measurability of abstract issues (van de Walle 2006; Andrews 2008; Neumann and Graeff 2010; Barendrecht 2011; Ginsburg 2011). Moreover, the global indices might not always be apt for observing grassroots developments, and therefore even undemocratic events or crises might remain under their radar (Hinthorne 2011; Morgan 2011). Also, the use of the good governance indicators has attracted interest, most notably concerning development funding (Stubbs 2009; Hammergren 2011; Saisana and Saltelli 2011; Knoll and Zloczysti 2012). As a result of this, there has been a shift from good governance rankings towards second-​generation measurements. The previous general-​level rankings have been complemented by more subject-​specific disaggregated 28

Global Rankings

Table 2.2: Rankings and second-​generation indicators as policy instruments Rankings

Second-​generation indicators

Presentation of results

Single aggregate figure

Disaggregated data (mapping)

Specificity of results

General information on systemic-​level

Detailed information on institutions and processes

Typical use

General-​level assessments, comparisons

Monitoring

Mechanism of influence

Naming and shaming, adherence to norm

Expert knowledge, peer pressure

Source: After Erkkilä (2016, 396)

measurements that also allow monitoring of effects. These metrics include mappings that measure transparency, such as the Global Integrity Report (ranking 2006–​2009, mapping 2010–​), Open Budget Index (2006), Open Net Initiative (2007) and Government at a Glance (2009). The Global Integrity Index is demonstrative of this development. The organization producing the index, Global Integrity, decided to discontinue its ranking in 2010 and instead launched a mapping that no longer builds on aggregation. Following this, several other organizations have published mappings that measure more specific aspects of governance. Table 2.2 summarizes the differences between rankings and the so-​called second-​generation indicators. Through the use of disaggregated figures and measurements of specific policy processes and institutions, the focus is shifting from general-​level assessments to closer monitoring of governance and its development. Here, the actual mechanism of influence is also changing from ‘naming and shaming’ to peer pressure and adherence to expertise. However, even though ranking as the means for data representation and logic of comparison has become reconsidered in the context of good governance metrics, it has found uses in other contexts such as the global comparative assessment of higher education.

Global university rankings In higher education, single league tables have provided their users (administrators, students, politicians, journalists) with objectified information in a rapidly growing international student market. Existing ranking systems represent key tools for higher education reform. In the US, evaluations of graduate programmes started already in the 1920s and a ranking of US colleges was published in 1983. The university rankings made their way to 29

Knowledge Alchemy

the UK in the 1990s. The rankings became internationally policy relevant in the 2000s, due to the marketization of higher education and increased mobility of students (Harvey 2008, 187–​188). Table 2.3 shows a selection of global university rankings, concerning their publisher, publication year and methodology. Two major university rankings are published by the Shanghai Jiao Tong University’s Institute of Higher Education and the Times Higher Education (THE). With the support of the Chinese government, which wanted to assess the performance of Chinese universities in comparison with the world’s best universities, Shanghai Jiao Tong University has been producing an institutional ranking on a yearly basis since 2003. This ranking focuses on ‘measurable research performance’ (Liu and Cheng 2005, 133). It is particularly favourable to universities in English-​speaking countries: they represented 71 per cent of the world’s top 100 universities in 2006. US-​based institutions alone occupy 17 of the world’s 20 top ranked universities. The narrative behind the creation of Shanghai ranking frames it as an attempt to assess where the Chinese universities stood vis-​à-​vis the European and North American higher education institutions; but the move comes at the time of a global power shift where many Asian countries emerged to challenge Europe and the US economically while also investing heavily in education and research (Reinalda 2013). As a tool of performance evaluation, the Shanghai ranking hence has two potential references. On the one hand, it links to the five-​year plans that the Chinese Communist Party had and still uses for planning its economy. On the other hand, it associates with the field development of global ranking, where various other metrics had emerged earlier and national performance higher education measurement tools had existed for some time, for instance in the US (US News and World Report) and Canada (Maclean’s university rankings). In countries where tertiary education is not provided solely by public institutions, these national rankings offer much-​needed impartial data on higher education institutions for parents and future students. Like US News and World Report, the Shanghai ranking also gives yearly information on Chinese higher education institutions, as well as the growing population of middle-​class Chinese students planning to study abroad. In this sense, the real impetus for the development of the ARWU was the aim to modernize the Chinese economy. The ARWU was and is thus a tool that enables the steering of Chinese technology and science in a global direction, but it would come to shape the imagination of other students, parents, institutional leaders and policy makers around the world. Its antecedents were first the 211 plan which had 112 universities under it in 2015 (International Education 2015), then the 985 plan that included 39 universities, the C9 group that included the Chinese ‘Ivy league’ 30

newgenrtpdf

Table 2.3: Selected global university rankings Shanghai University Ranking of World Universitiesa

Times Higher Education Supplement Rankingsb

Webometrics Ranking of World Universitiesc

Affordability and Accessibility Comparison of Global Higher Education Rankingsd

Center for World-​Class Times Higher Education with Universities and the Institute career advice company of Higher Education of Quacquarelli Symonds Ltd Shanghai Jiao Tong University, China

Cybermetrics Lab at the Consejo Superior de Investigaciones Científicas, Spain

Educational Policy Institute, North America

Year

2003–​

2004–​2009e

2004–​(twice yearly)

2005, 2010

Indicators

- Alumni from institution with Nobel prize or Fields medal (10%) - Staff from institution winning Nobel prize or Fields medal (20%) - Highly cited researchers (20%) - Papers published in Nature and Science (20%)f - Science Citation Index-​ Expanded and Social Science Citation Index (20%) - Per capita academic performance of an institution (10%)

- Academic peer review (40%) - Visibility (50%): number of - Citations per research staff external networks (subnets) (20%) linking to the institution’s - Employer review (10%) webpages (normalized and then - International faculty the maximum value is chosen) index: percentage of - Transparency (10%): number international staff (5%) and of citations from top 210 students (5%) authors (excl. top 20 - Faculty staff–​student ratio outliers) (Source: Google (20%) Scholar Citations)g - Excellence (40%): number of papers among the top 10% most cited articles in 27 disciplines (Source: SCImago)

Accessibility indicators: - Participation rates - Attainment rates - Educational Equity Index - Gender Parity Index Affordability indicators: - Education costs as a percentage of Ability To Pay (ATP) - Total costs as a percentage of ATP - Net costs as a percentage of ATP - Net cost after tax expenditure as a percentage of ATP - Out-​of-​pocket costs as a percentage of ATP - Out-​of-​pocket costs after-​tax expenditures as a percentage of ATP (continued)

Global Rankings

31

Publisher

newgenrtpdf

Table 2.3: Selected global university rankings (continued) The SCImago Institutions Rankingi

QS World Times Higher Education University Rankingj –​Thomson Reutersk

Publisher

The Centre for Science and Technology Studies, Leiden University

The SCImago research group, Spain

Quacquarelli Symonds Times Higher Education and data provider Thomson Reuters

Year

2007–​

2009–​

2010–​

2010–​

Indicators

-​ Number of publications (Source: Web of Science) -​ Size-​dependent vs. size-​ independent indicators -​ Impact indicators (citations and their relative frequency) -​ Collaboration indicators (co-​authored publications) -​ Open Access indicators (proportion of Open Access publishing) -​ Gender indicators (gender diversity in authorship)

Research (50%): -​ Normalized impact (13%) (field normalized citation score) -​ Excellence with leadership (8%) -​ Publication output (8%) (Source: Scopus) -​ Scientific leadership (5%) -​ Not own journals (3%) -​ Own journals (published by the institution) (3%) -​ High quality publications (Q1) (2%) -​ International collaboration with foreign institutions (2%) -​ Open Access (2%) -​ Scientific talent pool (2%)

-​ Academic reputation (40%) -​ Employer reputation (10%) -​ Student-​to-​faculty ratio (20%) -​ Citations per faculty (20%) (Source: Scopus) -​ International faculty ratio (5%), international student ratio (5%)

Teaching (the learning environment) (30%): -​ Reputation survey (15%) -​ Staff-​to-​student ratio (4.5%) -​ Doctorate-​to-​bachelor’s ratio (2.25%) -​ Doctorates awarded-​to-​academic-​ staff ratio (6%) -​ Institutional income (2.25%)

Innovation (30%): -​ Innovative knowledge (10%) -​ Patents (patent applications) (10%) -​ Technological impact (10%) (percentage of publication output cited in patents)

Research (volume, income and reputation) (30%): -​ Reputation survey (18%) -​ Research income (6%) -​ Research productivity (6%) Citations (research influence) (30%)

Knowledge Alchemy

32

The Leiden Rankingh

newgenrtpdf

Table 2.3: Selected global university rankings (continued) The Leiden Rankingh

The SCImago Institutions Rankingi

QS World Times Higher Education University Rankingj –​Thomson Reutersk

Societal impact (20%): -​ Altmetrics (10%) -​ Web size (institution’s domain) (5%) -​ Inbound links (5%)

International outlook (staff, students, research) (7.5%): -​ Proportion of international students (2.5%) -​ Proportion of international staff (2.5%) -​ International collaboration (2.5%)

33

Notes: a Academic Ranking of World Universities Methodology 2021, https://​www.shan​ghai​rank​ing.com/​meth​odol​ogy/​arwu/​2021 (Accessed 10 December 2021). b c

Source: Kauppi and Erkkilä (2011).

Webometrics Methodology, https://​www.webo​metr​ics.info/​en/​Meth​odol​ogy (Accessed 10 December 2021).

d Affordability and Accessibility Comparison of Global Higher Education Rankings, http://​www.educat​iona​lpol​icy.org/​pdf/​glo​bal2​005.pdf (Accessed 28 February 2013). e Since 2010 continued as QS World University Ranking. f For institutions specialized in humanities and social sciences such as London School of Economics, N&S is not considered, and the weight of N&S is relocated to other indicators. g Transparent Ranking: Top Universities by Google Scholar Citations, http://​www.webo​metr​ics.info/​en/​node/​169 (Accessed 30 June 2017). h Leiden Ranking Indicators, https://​www.leiden​rank​ing.com/​info​r mat​ion/​ind​icat​ors (Accessed 10 December 2021). i

SCImago Institutions Rankings Methodology, https://​www.scimag​oir.com/​meth​odol​ogy.php (Accessed 13 December 2021).

j QS World University Rankings Methodology, https://​www.topu​nive​rsit​ies.com/​qs-​world-​uni​vers​ity-​ranki​ngs/​meth​odol​ogy (Accessed 13 December 2021). k World University Rankings 2016-​2017 Methodology, https://​www.times​high​ered​ucat​ion.com/​world-​uni​vers​ity-​ranki​ngs/​meth​odol​ogy-​world-​uni​vers​ity-​ranki​ ngs-​2016-​2017 (Accessed 30 June 2017).

Global Rankings

Industry income (knowledge transfer) (2.5%)

Knowledge Alchemy

universities that ended in 2014, and the recent World Class 2.0 and Double First Class University (which includes the 39 universities from the 985 plan plus Yunnan, Xinjiang and Zhengzhou universities) and Discipline plans, which are all aimed at selecting the best from the roughly 2,000 Chinese higher education institutions. Chinese higher education policy planning stretches all the way to 2049, the 100th year anniversary of the coming to power of the Chinese Communist Party, when China aims to become, in the words of its leader Xi Jinping, ‘the most innovative country in the world’ (quoted in Economy 2018). As Shengbing Li, director of the Centre of Higher Education, South China Normal University, writes, China’s purpose is: [T]‌o lead the number and capacity of world-​class universities and disciplines with the world’s best, becoming a higher education power house by 2050. Tsinghua University aims to be a world-​class university by 2020, at the forefront of world class universities by 2030 and one of the world’s best universities by 2050. (Li 2018) As an unintended consequence, the ARWU reformatted the rules of global academic competition by providing a new vocabulary, like world-​class university, and new governance tools like global university rankings. It basically updated the informal reputational hierarchies that had existed in the academic world. Updating meant not just reproducing these informal and implicit hierarchies but above all transforming them into precise, data-​ based ordinal motivational deficit lists. ARWU claimed to provide a more ‘scientific’ view of the quality of higher education institutions. Since 2003, the key factor behind the surge of the global ranking business has been the rapid development of data and data tools provided by a variety of private companies such as QS, Thomson Reuters, Clarivate Analytics, Elsevier, and so on. Data and data tools provide these companies with a new kind of resource, digital capital, that can be converted into economic capital (cf Kauppi 2020). The first Times Higher Education Supplement (THES) entitled ‘World University Rankings’ was published in 2004. One of the driving forces behind the establishment of the league table was a perceived rising demand, in the UK and globally, for advice on higher education (Jobbins 2005, 137). The original THES ranking was discontinued in 2010, when THE changed its knowledge partner and subcontractor for producing the ranking and teamed up with Thomson Reuters to produce the current THE ranking. The former knowledge provider of the THES ranking, Quacquarelli Symonds, continued its product under the name of QS World University Ranking. In contrast to the Shanghai ranking, the THE composite index partly rests on present reputation (see Table 2.3), thereby reproducing established 34

Global Rankings

global reputations. Both the Shanghai and THE lists create a similar global order in which American universities tend to do the best, thus universalizing certain research university models. In the THE ranking, UK and Australian universities fare better than in the Shanghai ranking. Continental European universities are badly positioned in both university league tables.3 Since the publication of the Shanghai ranking there has been a tremendous increase in the number of global university rankings. At present, there are about a dozen university rankings of global scope, though many of them enjoy little media publicity (see Table 2.3). For example, there are attempts at measuring the web-​visibility of universities by the Webometrics Ranking of World Universities, also using Google Scholar publication data. There are also rankings of national higher education institutions in Taiwan (Higher Education Evaluation and Accreditation Council of Taiwan), the Netherlands (Leiden University) and Australia (University of Western Australia) that tend to focus on the research output of universities while giving less emphasis to teaching and learning. SCImago research group’s measurement also emphasizes research output in bibliometric terms as well as patents and universities’ web presence. Practically all global university rankings compare the research output of university academics and researchers. The North American Educational Policy Institute makes an exception by having produced the only global ranking to assess national systems instead of higher education institutions, and focusing on the affordability and accessibility of higher education. This provides an alternative view of the matter of higher education rankings, where the Nordic and Central European university systems are ranked higher than the Anglo-​American and Asian ones. But the North American Educational Policy Institute’s assessment is marginal and mostly not even known to the actors in the field. It has only been published twice, in 2005 and 2010. Drowned out by the assessments on the research performance of higher education institutions, the attempts to consider the role and mission of universities from another perspective have little chance of attaining widespread, if any, visibility. The global university rankings have been heavily debated in Europe, particularly in France, where the ‘underperformance’ of French universities in international rankings has been a constant theme in the media (Kauppi 2022). During the French European Union (EU) presidency in 2008, the European Commission declared that it would create an alternative university ranking, a European ranking list of world universities that

3

A certain irritation and frustration are reflected in the statements of the European political actors involved in higher education (see, for example, Pécresse 2008). 35

Knowledge Alchemy

would ‘do justice’ to European universities.4 To this end, the European Commission funded U-​Multirank by the Consortium for Higher Education and Research Performance Assessment. U-​Multirank provides a new type of mapping tool for comparing higher education institutions globally (see Table 2.4). The main difference between U-​Multirank and other rankings is that U-​Multirank does not provide an aggregate figure (ranking) but instead allows its user to choose the aspects of comparison and determine their preferred ratings. This bears a resemblance to the second-​generation indicators of good governance that also provides an opt-​out of making a single ranking list. Another non-​aggregated addition to the field of ranking is the Assessment of Higher Education Learning Outcomes (AHELO) by the OECD (see Table 2.4). AHELO assesses learning outcomes in higher education, not research output.5 Its feasibility study was published in 2013, covering 17 countries and 248 higher education institutions. AHELO does not provide a ranking and instead of league tables allows the participating institutions to benchmark their performance against their peers. To summarize, the global university rankings have steered the international debate towards focusing on individual institutions and not higher education systems. This has emphasized an individualistic understanding of higher education in which individual institutions are conceptualized as competing globally. In the field of global ranking, university rankings are ideationally linked with the rankings of good governance and economic competitiveness, seeing higher education as a competition on research output between higher education institutions. As with good governance indicators, there are methodological shifts towards disaggregated figures, but no real challenges to the epistemic knowledge shared by the actors. Despite new actors entering the field, the most prominent rankings by small actors that have entered the field of ranking at the early stage are effectively eclipsing prominent international actors such as the EU and OECD. Moreover, in terms of methodology, ranking as means of data representation is still overshadowing the disaggregate mappings and the focus of analysis remains heavily on the research output of individual higher education institutions. This has had wide ranking effects for higher education, where the competitive logic of ranking has become embedded in institutional practices, particularly concerning academic and administrative activities.

4

5

According to Director General of Education in the European Commission, Odile Quintin (quoted in Dubouloz 2008, 1). Testing Student and University Performance Globally: OECD’s AHELO –​OECD, http://​ www.oecd.org/e​ du/s​ kil​ ls-b​ eyo ​ nd-s​ cho ​ ol/t​ estingstudentanduniversitype​rfor​manc​eglo​ball​ yoec​dsah​elo.htm (Accessed 6 June 2017). 36

Global Rankings

Table 2.4: Non-​aggregated indicators of higher education The Assessment of Higher Education Learning Outcomesa

The U-​Multirankb

Publisher

OECD

U-​Multirank Consortiumc

Year

Feasibility study 2013

2014–​

Indicators

-​Generic skills of students -​ Discipline-​specific skills (economics and engineering) -​Contextual information

-​ General -​Teaching and learning -​ Research -​Knowledge transfer -​International orientation -​Regional engagement

Notes: a AHELO Main Study, https://​www.oecd.org/​educat​ion/​ski​lls-​bey​ond-​sch​ool/​ahelo-​main-​ study.htm (Accessed 12 December 2021). b

c

U-​Multirank, Catalogue of Indicators, https://​www.umu​ltir​ank.org/​about/​meth​odol​ogy/​ ind​icat​ors/​ (Accessed 12 December 2021).

U-​Multirank, The Consortium, https://​www.umu​ltir​ank.org/​about/​u-​multir​ank/​the-​con​ sort​ium/​ (Accessed 12 December 2021).

Source: Based on Erkkilä (2016)

World-​class university, conventions and shifting strategies of higher education As discussed in Chapter 1, policy scripts are generic policy models that define specific but generalizable measures to address a policy issue (Kentikelenis and Seabrooke 2017). The generalizability and prescriptive nature of such scripts has intensified through the use of indicators. For administrators and politicians, university rankings have become an indispensable part of policy planning (see, for instance, Bourdin 2008; Harvey 2008). As tools of symbolic power, ranking lists reinforce, for certain users, preconceived ideas about university excellence and the models to emulate. For others not familiar with higher education, they present a certain state of affairs as being scientifically proven and thus inevitable, shaping in this way the reality in the field of higher education. These ranking lists, reproduced by a variety of think tanks (see, for instance, Aghion et al 2007), present similar recipes for success in higher education: ‘autonomization’ of universities, concentration of resources through the creation of poles of excellence, and greater funding for certain types of research through research and development (R&D) investments. This alchemical recipe has been extensively integrated into reforms in higher education, as well as policy developments in other sectors such as 37

Knowledge Alchemy

immigration. In fact, the Shanghai formula for world-​class university is codified in the attributes of the ranking (compare Table 2.3): • • • • •

alumni from institution with Nobel prize or Fields medal (10 per cent); staff from institution winning Nobel prize or Fields medal (20 per cent); highly cited researchers (20 per cent); papers published in Nature and Science (20 per cent); Science Citation Index-​Expanded and Social Science Citation Index (20 per cent); and • per capita academic performance of an institution (10 per cent). In a survey among heads of French institutions of higher education, it was disclosed that 71 per cent of respondents found the ranking lists useful, 66 per cent wanted to improve their institutions’ position in the rankings, and a majority said that they knew how to do that (Bourdin 2008, 65). In Finland, an initiative to merge three universities used university rankings to identify the institutional characteristics of a ‘top university’ (Opetusministeriö 2007, 32–​39). The single league table presents a clear, ‘objective’ order, a goal to emulate and the means to attain this goal. Ellen Hazelkorn’s research (2011), covering 41 countries and 202 institutions, indicates that rankings are well known to the managers of higher education institutions, which commonly respond to them by adjusting to their injunctions. For example, 63 per cent of the institutions that took part in Hazelkorn’s survey had taken strategic, organizational, managerial or academic action in response to international rankings (Hazelkorn 2011, 96). Over 80 per cent wanted to improve their position in international rankings (Hazelkorn 2011, 86). The findings of Locke et al (2008, especially ­chapter 4) concerning British higher education institutions (n=​91) paint a rather similar picture. These developments are part of the broader shift towards embracing internationalization in the higher education domain. The Fifth Global Survey of International Association of Universities (Marinoni 2019, 66, 117) reports that 91 per cent of the survey respondents around the world declared that their institutions have an internationalization policy in place, with 56 per cent having developed this strategy in the last five years. For higher education institutions, rankings and internationalization are closely aligned. There are also conceptual shifts in the EU’s higher education strategy, as accountability of higher education institutions is now primarily assessed in terms of performance, which further reinforces the ideology of competition upheld by rankings (Erkkilä and Piironen 2013). Scholars have drawn attention to the global drafting of a world-​class university model (Mittelman 2017; Rider et al 2020) that has come to shape the higher education landscape (Kehm and Stensaker 2009; Hazelkorn 2011; 38

Global Rankings

Erkkilä 2013). While the world-​class university model has been defined and promoted by actors such as the World Bank (Salmi 2009), the rankings have helped to identify the attributes of world-​class university. The rankings also construct an imaginary of global competition in higher education, including models to idealize, goals to obtain and means to attain them. Erkkilä and Piironen found in a comparative study that the competitive logic of rankings has entered the strategies of European higher education institutions, regardless of whether they are in a credible position to compete globally (Erkkilä and Piironen 2020b). This study compared strategies of 27 European universities in the UK, Nordic and German context. The universities analysed represented three different ‘performance categories’ based on their ARWU rank (ranked 50–​150, 401–​500, unlisted). As Table 2.5 shows, the references to global university rankings have increased in the timeframe analysed. In 2018, there were only six universities in the sample of 27 that do not refer to global rankings, while there were 13 such cases in 2014. Interestingly, in 2018 the references to global rankings are more implicit, most notably in the UK and German institutions ranking 50–​150 in ARWU. This shows the difficulty of anticipating one’s rank scores with sheer academic performance. At the same time, the logic and discourse of competition was more inherently present in the strategies of the universities. Though gaming the rankings is not straightforward, there have been successful attempts to do this. A successful strategy has been to pay a premium to highly cited scholars for adding a university as a secondary affiliation in all of their publications. King Abdulaziz University (KAU) in Jedda, Saudi Arabia successfully used this strategy of transforming cash for prestige. To the surprise of many mathematicians (Pachter 2014), this unknown institution was ranked 6th in ARWU mathematics in 2015 while it was in the group ranked 51–​72 in 2012. This unlikely metamorphosis was achieved by buying highly cited researchers’ products, or rather their citations (Bhattacharjee 2011). Hired as ‘Distinguished Adjunct Professors’ at KAU, these highly cited scholars listed KAU as their secondary affiliation in their publications for an annual yearly fee of up to US$100,000. They had no other connections with KAU. One of them defended his decision by declaring ‘it’s just capitalism. … They have the capital, and they want to build something out of it’ (Neil Robertson, professor of mathematics at Ohio State University, quoted in Bhattacharjee 2011, 1344). But the most successful example of gaming the system comes from France, where Paris-​Saclay literally rose from nowhere to take a top-​20 position in the Shanghai ranking. This case shows both the pressure to adhere to the global norm, but also the extensive strategies of revaluing the existing institutions and their performance through a successful reclassification strategy. 39

Knowledge Alchemy

Table 2.5: References to international rankings in 2014 and 2018 Country ARWU bloc rank (2014)

Reference to global rankings 2014 (No/​Implicit/​ Explicit)

Reference to global rankings 2018 (No/​Implicit/​ Explicit)

University of Nottingham

UK

50–​150

Explicit

Implicit

University of Birmingham

UK

50–​150

Explicit

Implicit

University of Liverpool

UK

50–​150

Explicit

Explicit

University of Essex

UK

401–​500

Implicit

Implicit

University of Surrey

UK

401–​500

Implicit

Explicit

Swansea University

UK

401–​500

No

Explicit

Anglia Ruskin

UK

unlisted

No

Implicit

Sheffield Hallam

UK

unlisted

No

Implicit

Central Lancashire

UK

unlisted

Explicit

Implicit

Uppsala University

Nordic

50–​150

Implicit

Explicit

Stockholm University

Nordic

50–​150

Explicit

Explicit

Lund University

Nordic

50–​150

Implicit

Implicit

University of Eastern Finland

Nordic

401–​500

Explicit

Explicit

University of Jyväskylä

Nordic

401–​500

Explicit

Implicit

University of Tromsø

Nordic

401–​500

No

Implicit

Linnaeus University

Nordic

unlisted

No

No

University of Luleå

Nordic

unlisted

No

Implicit

Malmö University

Nordic

unlisted

No

Implicit

Technical University Munich

Germany 50–​150

Explicit

Implicit

LMU Munich

Germany 50–​150

No

No

Heidelberg University

Germany 50–​150

Implicit

No

Friedrich Schiller University Jena

Germany 401–​500

Explicit

Implicit

University of Duisburg-​Essen

Germany 401–​500

No

Explicit

University of Hannover

Germany 401–​500

No

No

Saarland University

Germany unlisted

No

No

Technical University Dortmund

Germany unlisted

No

Implicit

University of Kassel

Germany unlisted

No

No

Source: Erkkilä and Piironen (2020b)

40

Global Rankings

University Paris-​Saclay: a Cambridge à la française or a university of the future? The publication of the Shanghai ranking in 2003 sent shockwaves throughout the European higher education field and certainly not least in France, where the poor ranking performance of French higher education institutions has been a major policy issue ever since. This has caused reflexivity over the rankings and attempts to assimilate their idealized model of a ‘world-​class’ research university. The ironic quote from sociologists Neveu and Surdez captures the limited rationality of these efforts: When only a few French universities scraped a place in the ‘Shanghai ranking’ of the world’s universities there was a flurry of public policies of which one of the funniest was the notion that merging under-​ resourced universities and institutions into giant groups would magically allow them to break into the top twenty. (Neveu and Surdez 2020, 8; emphasis added) In this common-​sense logic, quality will not improve by producing more of the same. However, that is exactly what happened! Magic works as several partly merged French collegiate universities, like Sorbonne University, are now ranked as world-​class universities (cf Kauppi 2018; 2022; Shanghai ranking 2020; for analysis). By playing the ranking game they succeeded in being transmuted into world-​class universities, as the example of Paris-​Saclay University shows. In 2020, Paris-​Saclay, a newcomer to the French higher education and the global rankings, was positioned 14th in the Shanghai ranking. A year later, in 2021, it kept its position in the top-​20 and rose to 13th in the Shanghai ranking, sparking praise from the French president Emmanuel Macron, who now sees Paris-​Saclay’s success as a proof of French excellence in higher education: ‘30 French universities are recognized among the best in the world in the Shanghai ranking, including Paris-​Saclay, which takes 13th place. With 25 billion euros invested over 10 years, we will continue to make France one of the great scientific nations!’ (Macron 2021, authors’ translation). Similar comments were made by the President of Université Paris-​ Saclay, Sylvie Retailleau, who stated that the Paris-​Saclay’s high ranking on the Shanghai list ‘demonstrates the potential of French higher education and research to national and international students and the academic and socio-​economic world, and shows that [Paris-​Saclay’s] institutional model is successful’ (Université Paris-​Saclay 2021). Furthermore, Retailleau also notes that: The ranking is not an end in itself and it does not change our objectives. However, it does offer a fresh perspective on French higher education. 41

Knowledge Alchemy

It is a way of attracting new talent, showcasing our academic teams and the socio-​economic partners who work alongside us, and encouraging those who would like to contribute with us to the quality of higher education in France. (Université Paris-​Saclay 2021) These quotations are from a news item celebrating the 2021 ranking success of Paris-​Saclay (Université Paris-​Saclay 2021). Interestingly, the news bulletin also contains a summary of the Shanghai ranking attributes and a count of Paris-​Saclay’s Nobel prize laureates (2), Fields medallists (10) and highly cited researchers (33), all of which are measured by the ranking (see Table 2.3). To understand the importance of Paris-​Saclay for French higher education, we need to consider the policy debates and actions leading to the creation of this institution. This also corresponds with knowledge alchemy –​the attempt of transforming mundane practices and policies of knowledge governance into competitive ones following imagined global gold standards and universal symbolic formulas. Since the globalization of higher education at the beginning of the 2000s, and in response to the challenges it presents, the French university world has gone through a similar process as other European countries, most notably the further stratification of the national higher education system into two groups, those focusing on teaching and those focusing on research and international visibility, a dichotomy acknowledged by the first president of Paris-​Saclay, Sylvie Retailleau, as being a consequence of the Shanghai ranking (Le Nevé 2020). These changes have been linked to evolving power relations between French government ministries that have oversight of the various state-​funded higher education institutions, the Ministry of Defence for École Polytechnique de Paris and engineering schools like CentraleSupélec, the Ministry of Finance over schools like Télécom Paris Tech, and the Ministry of Universities over French publicly funded universities. Pressures on French higher education are thus always also political and administrative. Launched by French President Nicolas Sarkozy in 2008 and assisted in this by the then minister of universities, Valérie Pécresse, Paris-​Saclay University was supposed to gather the best in French academe to show the world how excellent French research really was. This exercise focused on a defining feature of French higher education: the co-​existence of a mosaic of small institutions that, individually, did not feature in university rankings like the Shanghai ranking, which focused on identifying the research outputs of comprehensive universities. The policy solution the French adopted was one revolving around the bringing together of individual, excellent entities. The organization model for Paris-​Saclay was the University of Cambridge as a collection of colleges, or in its French version a Comue or communauté d’universités et d’établissements (for analysis see Kauppi 2022). 42

Global Rankings

The ‘French Silicon valley’ or the French ‘Cambridge’ (Stromboli 2017) was supposed to house universities like Paris-​Sud, the only one listed in the top 50 in the Shanghai ranking in 2017, elite schools or grandes écoles like École Polytechnique and École normale supérieure Cachan, as well as large research organizations like the Centre National de la Recherche Scientifique (CNRS), the largest publicly funded fundamental research institution in Europe, and the Commissaire à l’énergie atomique (CEA), which has been renamed French Alternative Energies and Atomic Energy Commission. The goal was to create a mastodon of 76,000 students, 11,000 teachers and researchers, and 400 yearly patents, representing between 15 per cent and 20 per cent of the French research potential. The eventual ‘fusion’, which had national priority for both President Sarkozy and his successor François Hollande, quickly led to disputes between two camps, the École Polytechnique, on the one hand, and the universities on the other. In 2011, this tension prevented Paris-​Saclay from getting the Initiative d’excellence (IDEX), a national label that distinguishes those universities that can compete at the global level from the others, on the grounds that it did not present a real integrated university but merely a collection of institutions without a unified project. Thanks to the efforts of a new mediator nominated by the French government, Saclay finally got its IDEX label. The IDEX was launched by President Sarkozy in 2009 with the goal of creating a pool of around ten universities with a global status. It is mostly a way to channel funding, around €20 to 30 million yearly to each university community to promote ‘excellence’. While the Polytechnique finally prevented the French Cambridge from being created and set up, an alternative comue to that of Paris-​Saclay in the same geographical area south of Paris, Paris-​Saclay nevertheless has succeeded in reorganizing the French higher education landscape. In 2019, by governmental decree, Paris-​Saclay became an ‘experimental establishment’ for a period of ten years. As of 1 January 2020, Paris-​Saclay had 48,000 students and 9,000 researchers and teachers, representing 13 per cent of French research potential (Le Nevé 2020). This academic cluster is composed of four grandes écoles (AgroParisTech, CentraleSupélec, Ecole normale supérieure Paris-​Saclay and Institut d’optique Graduate School) and, as associated members, the universities of Versailles-​Saint-​Quentin-​en-​Yvelines and Evry, which are poised to fuse in 2025. Through a reinforced partnership, the CEA, CNRS, Institut national de la recherche agronomique, Institut national de recherche en sciences et technologies du numérique, Institut national de la santé et de la recherche médicale, Office National d’Etudes et de Recherches Aérospatiales and Institut des hautes études scientifiques are also included in the University Paris-​Saclay. The plan to create a community of higher education institutions without a total fusion hinged crucially on the global rankers’ willingness to count 43

Knowledge Alchemy

these institutions, and 11 other similar communities in France, as one unit. Revealingly, French authorities contacted perhaps the most prestigious of these global rankers, the Shanghai-​based ARWU, and made sure these experimental quasi-​fusions would be considered as forming single higher education units, and thus eligible for ranking assessment and world-​class university status. In the spring of 2019, the Ministry of Higher Education and Research had to convince the ARWU of the relevance, regarding the criteria of its ranking, of this new French university mapping, 12 experimental establishments having emerged. A request that the Chinese company finally acceded to in June (Le Nevé 2020). As the case of Paris-​Saclay University shows, rankers like ARWU and the company producing the ranking (Shanghai Consulting Agency) are today the real arbiters and knowledge alchemists of higher education, at global and national levels. Sarkozy’s wager has been successful. In 2020, despite a modest budget of €900 million, Paris-​Saclay was positioned 14th in the Shanghai ranking (Le Nevé 2020), the best position for a French university since the creation of the ranking in 2003. Already in a thematic classification of ARWU earlier in 2020, Paris-​Saclay had been rated first in math, ninth in physics, and was in the list of 25 best universities in agriculture and medicine. Because of Paris-​Saclay and other French university communities, President Macron proposed in his Sorbonne speech in 2017 that the approach of having ‘university communities’ be adopted at the European level. Currently, 41 European university alliances, consisting of a minimum of three higher education institutions from three EU member states (European Universities 2020), develop a new type of transnational university that aims at being the university of the future. Knowledge alchemy involves the creation of value from one form of knowledge to another. Such valuation takes place through classifications where a qualitative distinction is created between the excellent and the ordinary. These classifications produce social value and determine the present worth of objects, and, as we have shown, also their future worth. As the example of Paris-​Saclay demonstrates, simply complying with and successfully gaming the criteria of the Shanghai ranking can help to create an institution of excellence without actual changes in the academic performance of organizations and individuals involved.

Conclusions Policy scripts as generic policy models define specific but generalizable measures to address a policy issue, while prescribing action (Kentikelenis and Seabrooke 2017). The prescriptive nature of global policy scripts has intensified through the use of indicators and the field development of global 44

Global Rankings

ranking. Rankings allow comparisons and shared understanding of goals, but instead of only working as tools of evaluation, they also have constitutive effects (Kauppi and Erkkilä 2011, Dahler-​Larsen 2014), steering the activities of those being ranked. University rankings are now not only being referred to in the strategies of universities, but the ideas of the world-​class university and imaginary of competition also now inform the underlying discourse and rationalities of such strategies. There are attempts, sometimes even successful, to game the rankings by institutional restructuring that takes the rankings as their guidelines. While there certainly has been idealization of –​ for ­example –​the ‘MIT model’ in the past, leading to university mergers, the Paris-​Saclay case shows more profound commitment and observation of the ranking criteria as a script that leads to a data-​driven reform resembling an alchemist’s formula for gold. The introduction and implementation of the European Universities Initiative following French President Macron’s promotion of the Paris-​Saclay formula of bringing together ‘excellent’ institutions reveal how profoundly powerful the externally developed ranking instrument has been in steering national and regional knowledge governance. It is important to understand such actions contextually as global higher education has been actively linked to the ideas of competitiveness and innovation, where the previous ranking results, methodology and data are now actively being used. Such field development in global ranking (Kauppi and Erkkilä 2011; Erkkilä and Piironen 2018) now constitutes path dependencies in numerical governance, where new indicators emerging to challenge the existing ones often come to reproduce the existing practices of knowledge production. This is particularly visible in the city-​level assessments of innovation and competitiveness, discussed in Chapter 3.

45

3

Human Capital and the Rise of the Global Talent Competition Introduction In the previous chapter, we discussed the field development in global ranking concerning good governance indicators and university rankings. In this chapter, we turn to discuss innovation rankings and city-​level measurements of competitiveness that emerged a few years later. As our discussion will show, they have come to draw heavily from previously published datasets, hence echoing the hegemonic views and ideological undercurrents already present in the field as we have outlined in the previous chapter. The sharing of data is hence part of the evolving conventional power of data production on a global level. Empirically, we focus on four key indicators of knowledge governance and competitiveness that are also revealing of how the global ranking field has evolved: Global Competitiveness Index (GCI), Global Innovation Index (GII), Global Talent Competitiveness Index (GTCI) and Global Power City Index (GPCI). We are particularly interested in the idea of ‘global talent competition’, where countries and cities are now competing over talented individuals, linked with world-​class educational and innovation systems (see also Chapters 5 and 6). We argue that the field development of ranking has strong implications for the creation of global policies on education, innovation and AI as the major ranking producers are explicitly revising their indicators to analyse the social transformations anticipated through automation and AI (World Economic Forum 2019c; INSEAD et al 2020) or using AI as a motivation for launching new indicators (Tortoise 2019a; World Bank 2019). The number of indicators has thus increased, and their nominal focus has expanded, but they strongly converged, both conceptually and in terms of the use of data between different indicator sets. They are now

46

Human Capital and the Global Talent Competition

coming together under the notions of ‘AI’ and ‘talent competition’. The knowledge alchemy we observe in this process is the use of existing data and concepts that strongly project past ideas and ideals of governance for the automated future, or what it is assumed to be. This means that there is a strong sense of conformity concerning the assessments of uncertain future through automation and the use of AI. Indicators are becoming a lingua franca for global governance, not only in the domains we commonly associate with knowledge (Espeland and Sauder 2007; Kelley and Simmons 2015; Merry et al 2015). Relevant rankings are known by everyone in a policy field and allow comparisons and shared understanding of goals. But they also prescribe actions for improving one’s position in the rankings. Numbers function as instruments of objectification (Desrosières 1998, 9), giving shape and meaning to the objects of measurements and rendering them governable (Miller and Rose 1990; Robson 1992). While the figures are intended as tools of evaluation, they also have constitutive effects (Dahler-​Larsen 2014), imposing preconceived models of innovativeness, but also timeframes of action, leading to prioritization of certain activities or performance outcomes over others. The categorical rules of the assessments are steering the activities of innovation governance through reflexivity of those being ranked (cf Löwenheim 2008; Kelley and Simmons 2015). As mentioned, global policy scripts define specific but generalizable measures to address a policy issue (Kentikelenis and Seabrooke 2017). Unlike descriptive policy positions, the policy scripts prescribe action; this is because of how knowledge structures the scripts in ways that also further set predetermined sequences of events, based on storylines (Schank and Abelson 1977). The generalizability and predetermination of policy measures, as well as their prescriptive nature, has intensified through the use of indicators. Global rankings bring coherence to global governance by constructing and upholding the imaginary of global talent competition (cf Sum 2009; Alasuutari and Qadir 2016), but indicators also enable bridging ideas and data across policy domains (Erkkilä and Piironen 2018). This is apparent in the metrics analysed for this chapter that build heavily on data produced by a small number of organizations. We will demonstrate this with the help of network analysis on the GCI, GII, GTCI and GPCI. We will first analyse city rankings and how they have come to highlight cities as innovation hubs and how this uses university rankings as a proxy for local innovation. We then move on to analysing the convergence of the metrics of competitiveness, innovation and education and its implications on the policy script of ‘talent competition’. We will conclude the chapter with closer analysis of the actors behind the GTCI.

47

Knowledge Alchemy

Time and place of innovation The notion of innovation is historically speaking not uncontested, but it has nevertheless become a key policy term over the past decades. Benoît Godin’s analysis on the history of the concept shows how the notion of ‘innovation’ received negative connotations for centuries, but the concept started to receive positive connotations when it was reduced to ‘technological innovation’ intended to bring economic progress in the 20th century (Godin 2014; 2016). Moreover, stemming from social sciences and the language of practitioners, ‘technological innovation’ marks a departure from science and basic research by highlighting the application of research results for economic gain (Godin 2016). According to Godin (2016), the concept of ‘innovation’ can even be seen as a counter-​concept to ‘science’ in Koselleck’s terms (Koselleck 2004; Godin 2016, 529). While science exists for the sake of its own, involving exclusively the people in the scientific process, the concept of innovation is an inclusive one, comprising not only scientists but also a range of other actors and activities, including economists, managers, those well-​versed in intellectual property and other institutional actors. But, more importantly, innovation became framed as a process in time (Godin 2016, 540), as well as one of anticipation. This temporal element, perceiving innovation as a process, hence carries the normative expectation that there is an output, which is also to benefit the society at large. This further renders the actors involved responsible for their actions, not only in terms of scientific peer review, but increasingly or even exclusively for their ability to enhance economy. It also further deepens the conceptual division between innovation and basic research (science), as noted by Godin (2016). This has practical implications for still-​emerging scientific systems intending to produce cutting-​edge research: resource allocation is likely to favour research that generates scientific findings which can be translated into a product and brought to the market. Along temporal aspects, innovation also has more pronounced spatial elements. Scientific progress is in principle not bound in a particular location. Perceiving academic research as a collective activity engaged by the scientific community, it is irrelevant if the new knowledge is produced in Helsinki, Boston, Paris or Singapore –​all that matters is that progress is made. But innovation as a process involves various other actors and interests that are more spatially bound, often by the boundaries of a nation-​state –​apparent in the concept of national innovation system. However, the locus of innovation is not static and has been shifting over time, along with the actors involved. The concept of ‘national innovation system’ emerged in the 1980s, though its roots can be traced to the OECD’s work in the 1960s on ‘system approach’ (Godin 2009). The national innovation system perspective subsumed academic research to innovation by blurring the line between basic research and applied science, 48

Human Capital and the Global Talent Competition

but also rooted innovation to a particular national context with universities, government, industry and regulative environment, while at the same time emphasizing the links between science policy and other policy domains as well as international collaboration networks (Godin 2009). One aspect of the national innovation system paradigm was the aim to measure knowledge production and transfer within the ‘system’, leading to R&D statistics. There were also parallel developments leading to the emergence of ‘knowledge-​ based economy’ and ‘new economy’ that further highlighted the role of knowledge in national economic activities (Godin 2004; 2005). Higher education institutions and systems are now seen as key actors in national economic competitiveness, increasingly also with the help of global university rankings (Youtie and Shapira 2008; Erkkilä and Piironen 2013; Pelkonen and Teräväinen-​Litardo 2013). However, the locus of innovation has been recently shifting. While initially posited within a country, a national innovation system, the regional aspects of innovation have also been discussed and ranked, most notably by the European Commission (European Commission 2022). Recently, there has also been a conceptual shift from ‘regional innovation systems’ to ‘global innovation hubs’ (Malik et al 2021), where the ‘hubs’ are closely linked to educational systems as well as the mobility of skilled individuals who will then in turn ensure that the hubs operate according to design (Knight 2014; Knight and Lee 2014). This also reflects recent debates on competitiveness that now highlight regained interest in industrial policy and technological change through digitization and AI, calling for a broader understanding of economic activities and collaboration between enterprises, academia and the public sector, while also acknowledging regional policy, employment, migration and sustainability issues (Ketels 2006, 116–​118; Aiginger and Rodrik 2020, 191–​193, 202–​203; cf Porter 1990; 2003). This paradigm shift in industrial policy (Ketels 2006; Aiginger and Vogel 2015; Aiginger and Rodrik 2020) is also visible in the global models and metrics of competitiveness that are now more focused on innovation and its concentration in innovation hubs. The rankings also echo the new societal role of science and higher education institutions that are increasingly seen to be responsible to society at large for their contribution in innovation activities. The rankings also increasingly focus on urban actors (Kangas 2017), rendering them responsible for the innovation ‘process’ of the ‘hub’ in addressing the future challenges of knowledge governance and economic competitiveness.

City rankings: cities as innovation hubs As discussed in Chapter 2, the first global rankings mostly focused on countries. The university rankings were an exception, comparing higher 49

Knowledge Alchemy

education institutions. The focus of measurement has since shifted and the measurements of local innovation and competitiveness often hold cities as the subject of their analysis (Tables 3.1 and 3.2), complementing previous global rankings. Such city-​level or regional rankings typically include assessments of knowledge, talent and human capital. The city rankings claim to provide novel measurements of local innovation, but a closer look at these indicators shows how they build on previous global rankings and are thus a reconfiguration of existing measures. This is clearly visible in their concepts and attributes as well as sources of data. In other words, data sources actually target countries, and are generally not collected to address developments in cities. This use of indicators thus represents a form of knowledge alchemy. Interestingly, as universities are mostly located in urban areas, the university rankings are one of the few data sources that actually measure local institutions. Looking at Table 3.1, we see that there are a number of new data producers in the field of measurement, along with more recognized authorities such as the WEF and the Economist Intelligence Unit. Other organizations that produce city-​level rankings are mostly private companies and consulting firms (MasterCard, A.T. Kearney), business information providers (CrunchBase, Compass) and non-​profit organizations (the Mori Memorial Foundation). The first subnational competitiveness measurements in this sample were published by MasterCard (2007–​2008), A.T. Kearney (2008) and the Mori Memorial Foundation (2008). These were then shortly followed by the EU Regional Competitiveness Index (2010), Hot Spots 2025 (2013) and Competitiveness of Cities report (2014). These datasets supplement the earlier measurements of competitiveness, such as the WEF’s GCI, while also providing a broader context for analysing higher education and academic research. Innovation is also more firmly linked to economic activities, for example the Startup Ecosystem Report (first published in 2012) by Crunchbase and Compass shows a novel focus in competitiveness, namely the ecosystem of private and public actors. These are clear examples of how knowledge governance, generally understood as a state-​driven process, is now managed and administered by non-​state actors. Table 3.2 shows a sample of subnational innovation rankings. The European Union’s Innovation Union Scoreboard was published already in 2001, in the wake of the EU’s Lisbon Strategy. Renamed as the European Innovation Scoreboard, it also includes other European countries outside the EU. The annual report also contains a brief section on the global standing of the EU as a bloc vis-​à-​vis competitors such as the US and the BRICS countries (Brazil, Russia, India, China and South Africa) (European Commission 2016). There are also new innovation indexes that focus specifically on cities, such as the Innovation Cities Index, published by ‘data innovation agency’ 50

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Table 3.1: Competitiveness assessments: city and regional level Indicator Worldwide (producer) Centers of Commerce Indexa Role of leading cities in global economy (75 cities). Index and ranking based on seven dimensions:

51

City performance and City ‘magnetism’ outlook (125 cities). (40 cities). Index Global Cities Index: and ranking based on six functions: 1. Business activity 2. Human capital 1. Economy 1. Legal and 3. Information 2. R&D political framework exchange 3. Cultural 2. Economic stability 4. Cultural interaction 3. Ease of experience 4. Liveability doing business 5. Political 5. Environment 4. Financial flow engagement 6. Accessibility 5. Business centre Global Cities 6. Knowledge Outlook: creation and 1. Personal well-​being information flow 2. Economics 7. Liveability 3. Innovation 43 indicators and 74 governance sub-​indicators

Notes: MasterCard, 2007–2008 b A.T. Kearney, 2008– c The Mori Memorial Foundation, 2008– d EU Joint Research Centre, 2010, 2013, 2016 e The Economist Intelligence Unit, 2013 f CrunchBase, Compass, 2012, 2015, 2017 a

Global Power City Indexc

EU Regional Competitiveness Indexd

Hot Spots 2025e

The Startup Ecosystem Reportf

Competitiveness of EU regions. Index and ranking based on 11 pillars:

City competitiveness (120 cities). Index and benchmarking based on eight thematic categories:

Startup ecosystems of metropolitan cities or geographic areas with a shared pool of resources (40 ecosystems). Index based on five components:

1. Institutions 2. Macroeconomic stability 3. Infrastructure 4. Health 5. Basic education 6. Higher education 7. Labour market efficiency 8. Market size 9. Technological readiness 10. Business sophistication 11. Innovation

1. Economic strength 2. Physical capital 3. Financial maturity 4. Institutional character 5. Human capital 6. Global appeal 7. Social and cultural character 8. Environment and natural hazards

1. Performance 2. Funding 3. Talent 4. Market Reach 5. Startup experience

Human Capital and the Global Talent Competition

Focus of assessment

A.T. Kearney’s Global Citiesb

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Table 3.2: Innovation rankings for regional and city level Top 100 Global Innovatorsd

Top 100 Top 25 Global Innovative Universitiese Innovators –​ Governmentf

Focus of assessment

Innovative companies based on patent and citation data (top 100 listed). Ranking based on patent and citation data assessed across four main criteria:

Ranking of (top 100) universities based on patent and scientific-​ literature metrics. Ranking based on articles in scholarly journals and patent applications filed by the institution

Innovation performance of EU member states/​ regions. Index based on ‘enablers’, firm activities and ‘outputs’:

52

Enablers 1. HR 2. Open, excellent research systems 3. Finance and support Firm activities 1. Firm investment 2. Linkages and entrepreneurship 3. Intellectual issues Outputs 1. Innovators 2. Economic effects

Flows of R&D spending among companies and countries (1,000 highest spending companies globally). Ranking based on R&D spending

Cities potential as innovation economies (445 cities). Index based on three factors: 1. Cultural assets 2. human infrastructure 3. Networked Markets 31 segments and 162 indicators

1. Volume 2. Success 3. Globalization 4. Influence

Notes: a The European Commission, 2001–2008/ 2008– b 1000, PwC and Strategy, 2005– c Cities Index, 2thinknow, 2007– d Reuters & Thomson Reuters Intellectual Property & Science, 2011–2015 Clarivate Analytics, 2016– e Reuters & Thomson Reuters Intellectual Property & Science, 2015, 2016 f Reuters & Thomson Reuters Intellectual Property & Science, 2016, 2017

Publicly funded institutions (top 25) advancing science and technology. Ranking based on articles in scholarly journals and patent applications filed by the institution

Knowledge Alchemy

Indicator European Innovation Global Innovationc (producer) Scoreboard/​ Innovation 1000b Innovation Union Scoreboarda

Human Capital and the Global Talent Competition

2thinknow, which was first launched in 2007 to measure the potential of cities as ‘innovation economies’. In terms of conceptualization, data and methodology, the young city rankings resemble early measurements of good governance, which were composite indicators, using data from various sources (Knack et al 2003; Langbein and Knack 2010). However, there is also a concrete link in data sources, as these city-​level measurements mostly use available public data sources, instead of producing data themselves. The global good governance indicators now provide a major share of the data sources used by the city rankings (Erkkilä and Piironen 2020a). For instance, A.T. Kearney’s Global City Index uses country data in the absence of city-​level data (Leff and Petersen 2015, 12). While A.T. Kearney does not publish its data sources, there are clear links to existing governance indicators, such as the measurements of Freedom House (for example, A.T. Kearney 2014, 14). The Global Cities Outlook contains indicators on transparency, quality of bureaucracy, and ease of doing business, resembling the World Bank’s Worldwide Governance Indicators and Ease of Doing Business ranking. A.T. Kearney also appears as a ‘knowledge partner’ in the Global Innovation Index 2015 report (Cornell University et al 2015, preface). The EU’s Regional Competitiveness Index measures the competitiveness of regions within EU member states based on the so-​called NUTS 2 regional categories and ‘builds on the approach of the Global Competitiveness Index by the World Economic Forum’ (Annoni et al 2017, 2). In its measurements of national institutions, the Regional Competitiveness Index uses data from the World Bank’s Worldwide Governance Indicators and the Ease of Doing Business scores, as well as indicators from the WEF’s GCI.1 In similar fashion, the ‘Economy’ function of Mori Memorial Foundation’s Global Power City Index also draws sources from existing indicators such as World Bank’s Ease of Doing Business, Moody’s credit rating, Heritage Foundation’s Index of Economic Freedom, and the Global Talent Competitiveness Index by INSEAD. The new regional and city-​level measurements are not very specific in nature, as they often aim at making general assessments of very abstract concepts such as ‘innovation’ and ‘competitiveness’. This is surprising, as the narrower geographical focus on cities would in principle allow for creating indicators more specific to cities. But as their producers have limited resources and are perhaps therefore compelled to use available country data instead

1

The EU Regional Competitiveness Index 2016: indicators description, http://​ec.eur​ opa.eu/​regi​onal​_​pol​icy/​sour​ces/​docge​ner/​work/​rci​2016​_​ind​icat​ors.xls (Accessed 15 May 2017). 53

Knowledge Alchemy

of collecting city-​level data themselves, the result is the opposite. City-​level measurements are mostly composite indicators that often base their analysis on available country data. This data dependency further strengthens the links between established country rankings and emerging city indicators. Some, like 2thinknow, are tapping into this growing business. It offers through its Innovation Cities programme various service packages ranging from a simple benchmark indicator against competing cities to a City Change Plan that involves co-​creating a change plan based on 500 cities (2thinknow 2021). In short, as these examples indicate, the city-​level competitiveness indicators build directly on previous global measurements of competitiveness and innovation. What makes them stand out in their analysis is their use of university rankings or imitations of them. This allows them, at least at the level of argument, to focus on innovation hubs and major cities where global knowledge production mainly takes place. But even here, however, the objectification of innovation becomes a hostage of existing data (Erkkilä and Piironen 2020a).

Global talent competition and converging metrics of competitiveness, innovation and education The WEF has been one of the most prominent organizations arguing for addressing the societal challenges of AI. Since its publication in 2004, the WEF’s GCI has been influential in making economic competitiveness an active policy concern for countries. While the concept has been around much longer (Krugman 1994; Cerny 1997), the WEF’s ranking has made ‘competitiveness’ a knowledge brand that can be assessed and replicated across distinct institutional and regime contexts (Sum 2009) (see also Chapters 5 and 6). The launch of the fourth version of GCI in 2018 took place in the context of the WEF forcefully promoting its view of AI, bringing this also to national governments’ policy agenda. Figure 3.1 shows a network visualization of the data sources (by producers) of the GCI, GII, GTCI and GPCI. These four selected indicators measure competitiveness, innovation, talent mobility and city competitiveness, respectively. They have been produced over a timespan of approximately ten years. Despite the different focus and date of production, the rankings have great similarities in their composition and data sources. While the three indicator sets might not have exactly the same data, some 87 per cent of their data has been produced by the same ten international organizations (Erkkilä and Piironen 2020a), which is apparent also from Figure 3.1. There is a strong convergence in the causal and normative beliefs but also in the methodological choices and data (cf Haas 1992). Consequently, the indicators provide a uniform view of global knowledge governance and its measured entities, contributing to the global imaginary of competition 54

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Figure 3.1: Network of data sources (by producer), GCI, GII, GTCI, GPCI Numbeo Numbeo

PwC IHS Markit Markit IHS

Nestpick Nestpick

Lloyd’s

La Liste

KPMG Hotels.com Hotels.com

Tom Tom Tom

TripAdvisor TripAdvisor

UBS Rapisaniye Pogodi Pogodi Ltd Rapisaniye

Google

EF Education Education First EF First De Gruyter Saur De Saur Coworker.com Coworker.com Clarivate Analytics Clarivate Analytics Artprice.com Artprice.com Thomson Reuters Thomson Reuters

OAG OAG

Moody’s Moody’s

Artnetcom Artnetcom

Crunchbase Crunchbase Fubra Limited Limited Fubra

GPCI

Fortune Cushman & Wakefield Cushman Wakefield Columbus Travel Media Columbus Media

AmericanAssociation AssociationofofPort Port Authorities Authorities American TransparencyInternational International Transparency

App Annie Annie Intelligence App Intelligence

55

Times Higher Education Times Education InternationalBudget Budget Partnership Partnership International QS Quacquarelli Quacquarelli Symond QS Symond Ltd Ltd Linkedln SCImago Wikimedia Foundation Foundation Wikimedia Eurostat GCI Social Progress Progress Imperative Social Imperative Eu JRC EU JRC Mori Memorial Memorial Foundation Mori Foundation Cornell University University INSEAD Cornell GTCI Legatum Institute Legatum Institute World Bank World Bank Heritage Foundation Foundation Heritage Columbia University Columbia University Turku School School of Economics Turku Economics The Conference Conference Board The Board WittgensteinCentre Centre for for Demography Demography and Wittgenstein and Global GlobalHuman HumanCapital Capital Freedom House Freedom House Reporters Without Without Borders Reporters Borders Yale University University Yale GII Institute for for Health Health Metrics Metrics and Institute and Evaluation Evaluation national sources sources national World Economic Forum World Forum OECD UNESCO UNESCO World Health Health Organization World Organization BureauofofEconomic Economic Analysis Analysis Bureau United Nations United Nations International Monetary Monetary Fund International Fund International Labour Organization International Organization WorldIntellectual Intellectual Property Property Organization World Organization International Organization Organization for International for Standardization Standardization International Telecommunication Telecommunication Union International Union UNCTAD International UNCTAD International Trade Trade Centre Centre International Energry Energry Agency International Agency World Trade Trade Organization World Organization World Federation Federation of Exchanges World Exchanges Global GlobalEntrepreneurship EntrepreneurshipResearch Research Association Association International Air Air Transport Transport Association International Association(IATA) (IATA) International UnionforforConservation ConservationofofNature Nature (IUCN) (IUCN) International Union UNIDO UNIDO UnionofofInternational International Associations Associations Union United NationsPublic PublicAdministration Administration Network Network United Nations

Source: 2019 technical annexes of the indicators. The network visualization has been done with Gephi open source software.

Human Capital and the Global Talent Competition

ZookNIC Inc ZookNIC Inc

OpenStreetMap OpenStreetMap

Knowledge Alchemy

and giving a seeming unproblematic sense of orientation for the future. The figure also shows which datasets are the most used, that is, having the greatest impact in shaping global governance. As access and use of public datasets such as the ones provided by the World Bank have become easier, they also acquire greater mobility and impact compared to less mobile data and datasets. An unintended consequence is conformity and convergence in the use of datasets that becomes more institutionalized and automatized, entering a new phase in the ‘politics by numbers’ and knowledge alchemy where there are seeming global gold standards and universal formulas.

Global talent competition: human capital, mobility and innovation Figure 3.1 shows how the policy scripts on education, human capital and competitiveness now converge in the reports and benchmarking tools of global indicator producers such as the World Bank, Thomson and Reuters, INSEAD and the WEF. They now construct a formula for global talent competition: quality educational institutions, high-​ranking universities, educated workforce, and ability to recruit them from abroad, and a liberal ‘open society’ are now measured components of countries’ performance in ‘talent competitiveness’. What this formula implies is that the presence/​ absence of the individual components is a starting point for countries, cities and institutions to address, with those who are more advanced in their development moving on to focus on abundance. Indicator data helps to bridge policy fields as university rankings and OECD Program for International Student Assessment (PISA) results are now used as raw data for measurements of innovation, talent competition and city rankings, which also use data from indicators of good governance and human rights, linking human capital with liberal democracy and urbanization. Moreover, human capital is associated with academic mobility and migration (Chou 2021) and gender equality (Repo 2018). While competitiveness is the organizing concept for these assessments, education and research, also discussed as ‘human capital’, are at the heart of these scripts, being measured attributes of various indicators. This has strong implications for policy design (see Chapter 6). Tables 3.3–​3.5 show how the four indicators –​GII, GCI, GTCI and GPCI –​measure human capital, mobility and innovation, often appearing as sub-​indicators organized under a specific subheading or ‘pillar’ of the main indicator. We refer to this original structure in Tables 3.3–​3.5, showing, for example, how metrics of human capital can be found under GCI’s pillars 5 and 6. Human capital is largely reduced to educational levels, though there are also some references, interestingly, to health as well. Education is assessed as years in schooling, enrolment levels, and educational expenditure. 56

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Table 3.3: Indicators of education and human capital in GII, GCI, GTCI and GPCI

57

GII

GCI

GTCI

GPCI

2  Human capital and research 2.1 Education 2.1.1 Expenditure on education, % GDP 2.1.2 Expenditure on education, % GDP 2.1.3 School life expectancy, years 2.1.4 PISA scales in reading, maths, & science 2.1.5 Pupil-​teacher ratio, secondary 2.2 Tertiary Education 2.2.1 Tertiary enrolment, % gross 2.2.2 Graduates in science & engineering, % 2.2.3 Tertiary inbound mobility, %

Human capital Pillar 5: Health 5.01 Healthy life expectancy Pillar 6: Skills A. Current workforce I. Education of current workforce 6.01 Mean years of schooling II. Skills of current workforce 6.02 Extent of staff training 6.03 Quality of vocational training 6.04 Skillset of graduates 6.05 Digital skills among active population 6.06 Ease of finding skilled employees B. Future workforce I. Education of future workforce 6.07 School life expectancy II. Skills of future workforce 6.08 Critical thinking in teaching 6.09 Pupil-​to-​teacher ratio in primary education

3 Grow 3.1 Formal education 3.1.1 Vocational enrolment 3.1.2 Tertiary enrolment 3.1.3 Tertiary education expenditure 3.1.4 Reading, maths, and science 3.1.5 University ranking 3.2 Lifelong learning 3.2.1 Quality of management schools 3.2.2 Prevalence of training in firms 3.2.3 Employee development

Economy Human capital 7 Total employment 8 Employees in business support services Business environment 10 Availability of skilled human resources

5 Business sophistication 5.1 Knowledge workers 5.1.1 Knowledge-​intensive employment, % 5.1.2 Firms offering formal training, % firms 5.1.3 GERD performed by business, % GDP

4 Retain 4.1 Sustainability 4.1.3 Brain retention 5 Vocational and technical skills 5.1 Mid-​level skills 5.1.1 Workforce with secondary education 5.1.2 Population with secondary education 5.1.3 Technicians and associate professionals 5.2 Employability 5.2.1 Ease of finding skilled employees 5.2.2 Relevance of education system to the economy

R&D Academic resources 14 Number of researchers 15 World’s top universities

(continued)

Human Capital and the Global Talent Competition

Education/​ human capital

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Table 3.3: Indicators of education and human capital in GII, GCI, GTCI and GPCI (continued) GII

GCI

5.1.4 GERD financed by business, % 5.1.5 Females employed w/​ advanced degrees, % 5.3 Knowledge absorption 5.3.5 Research talent, % in business enterprise

GPCI

5.2.3 Skills matching with secondary education 5.2.3 Skills matching with secondary education 6 Global knowledge skills 6.1 High-​level skills 6.1.1 Workforce with tertiary education 6.1.2 Population with tertiary education 6.1.3 Professionals 6.1.4 Researchers 6.1.5 Senior officials and managers 6.1.6 Availability of scientists and engineers 6.2 Talent impact 6.2.1 Innovation output 6.2.5 Scientific journal articles

Knowledge Alchemy

58 Source: GCI (2019); GII (2019); GPCI (2019); GTCI (2019)

GTCI

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Mobility 59

GII

GCI

GTCI

GPCI

2 Human capital and research 2.2 Tertiary education 2.2.3 Tertiary inbound mobility, %

Pillar 8: Labour market A. Flexibility 8.07 Ease of hiring foreign labour 8.08 Internal labour mobility

2 Attract 2.1 External openness 2.1.3 Migrant stock 2.1.4 International students 2.1.5 Brain gain 2.2 Internal openness 2.2.2 Tolerance of immigrants

Cultural interaction International interaction 36 Number of foreign residents 37 Number of foreign visitors

Source: GCI (2019); GII (2019); GPCI (2019); GTCI (2019)

Human Capital and the Global Talent Competition

Table 3.4: Indicators of mobility in GII, GCI, GTCI and GPCI

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Table 3.5: Indicators of innovation in GII, GCI, GTCI and GPCI

Innovation

GCI

GTCI

GPCI

2.3 Research & development (R&D) 2.3.1 Researchers, FTE/​mn pop 2.3.2 Gross expenditure on R&D, % GDP 2.3.3 Global R&D companies, avg. exp. top 3, mn US$ 2.3.4 QS university ranking, average score top 3

Innovation Pillar 12: Innovation capability A. Diversity and collaboration 12.01 Diversity of workforce 12.02 State of cluster development 12.03 International co-​inventions 12.04 Multistakeholder collaboration B. Research and development 12.05 Scientific publications 12.06 Patent applications 12.07 R&D expenditures 12.08 Research institutions prominence index C. Commercialization 12.09 Buyer sophistication 12.10 Trademark applications

1 Enable 1.2 Market landscape 1.2.3 Cluster development 1.2.4 R&D expenditure 1.2.5 ICT infrastructure 1.2.6 Technology utilization

R&D Research environment 16 Research and development expenditure 17 Number of international students 18 Academic performance Innovation 19 Number of patents 20 Winners of prizes in science and technology 21 Startup environment

5 Business sophistication 5.2 Innovation linkages 5.2.1 University/​industry research collaboration 5.2.2 State of cluster development 5.2.3 GERD financed by abroad, % 5.2.4 JV-​strategic alliance deals/​bn PPP$ GDP 5.2.5 Patent families 2+​offices/​bn PPP$ GDP 6 Knowledge and technology outputs 6.1 Knowledge creation 6.1.1 Patents by origin/​bn PPP$ GDP 6.1.2 PCT patents by origin/​bn PPP$ GDP 6.1.3 Utility models by origin/​bn PPP$ GDP 6.1.4 Utility models by origin/​bn PPP$ GDP 6.1.5 Citable documents H-​index Source: GCI (2019); GII (2019); GPCI (2019); GTCI (2019)

Knowledge Alchemy

60

GII

Human Capital and the Global Talent Competition

What these three indices highlight is that there are three key elements in the alchemic formula for success in the global competition for talent: human capital (through education attainment and skilled worker retention), mobility (as inward flow of non-​citizens –​students, visitors, workers), and innovation (measured by R&D expenditure, university–​industry collaboration, bringing ideas to market). In many cities around the world, this formula has led to an interest in the notion of ‘innovation hubs’ where policy instruments are introduced, and institutions are geared towards developing and attracting human capital, ensuring inward mobility (a constant flow of talent who may be persuaded to stay and become part of the human capital) and fostering innovation.

Human capital, artificial intelligence and talent competition We have discussed how the field of global rankings has shifted from rankings of competitiveness and good governance to metrics of innovation and talent competition. More recently, there has been a shift towards the metrics of human capital and AI. This is visible in the metrics of competitiveness and innovation that are now being presented as reliable maps or compasses of the ‘fourth industrial revolution’, but there are also specific indicators for human capital and AI that are being produced to tackle the perceived future challenges of AI. Interestingly, they all endorse the talent competition paradigm. Table 3.6 shows indicators of human capital, well-​being, digital governance and AI. The emergence of the metrics on well-​being indicates the critique Table 3.6: Human capital, well-​being and digital governance indicators Pre-​2000 Human capital/​ well-​being

Digital governance

2000–​2009

2010–​

Human GLOBECO World Development Happiness Index (2000) Index (1990) Happy Planet Index (2006) Gallup-​Sharecare Well-​Being Index (2008) UN E-​Government Readiness/​Development Index (2003) UN E-​Participation Index (2003)

61

OECD Better Life Index (2011) UN World Happiness Report and Index (2012) Bloomberg Healthiest Countries Index (2013) Indigo Wellbeing Index (2016) Human Capital Index (2018) Government AI Readiness Index (2017) Global Cities AI Readiness Index (2019) Digital Government Index (2019) Global AI Index (2019)

Knowledge Alchemy

of gross domestic product (GDP) as a standardized measure (Stiglitz et al 2009). The OECD’s indicator set Better Life Index (BLI) is an example of indicators that followed the perception of valuing human ‘well-​being’ over GDP indicators. While well-​being is a broad and not clearly defined concept, the metrics of urban ‘liveability’, often part of city rankings, are to some extent also conceptually related to this growing trend. More recently, there has been a renewed interested towards human capital that was researched already in the 1950s and 1960s, particularly by Gary Becker (Becker 2009; Teixeira 2014). Though Becker’s work has been criticized for economizing various aspects of human life such as reproduction (Repo 2018), the World Bank in 2018 launched its Human Capital Index (HCI) that seeks to measure ‘new-​born’s expected amount of human capital by age 18’. Focused exclusively on health and education, the HCI appears like an outlier among the composite indicators that contain various data sources and conceptual sub-​elements. Table 3.7 shows the World Bank’s HCI next to the OECD’s BLI. Though the BLI is broader in its scope, both indicators focus on health and education. Interestingly, the report that accompanied the HCI ties it to digital technologies, automation and the changing nature of work that are seen as major challenges for governments around the world (World Bank 2019). This happens in the context where the WEF revises its GCI indicator and identifies it as ‘a compass’ for the ‘fourth industrial revolution’ (World Economic Forum 2018, v, 1–​2) and the producers of the GTCI promote it as a companion for ‘the age of AI’ (INSEAD et al 2020). There is now conceptual stretching, where human capital and talent competitiveness are strongly associated with digitalization and automation. Another recent trend in global rankings relevant to the talent competition paradigm are the indicators of AI. These metrics arguably measure countries’ or cities’ ‘AI readiness’. Table 3.7 also contains three such AI-​specific indicators, namely the Government AI Readiness Index (AIRI) by Oxford Insights, Global Cities AI Readiness Index (GCAIRI) by Oliver Wyman Forum and Tortoise media company’s Global AI Index (GAI) that arguably are the first rankings to assess countries’ and cities’ readiness and capacity for artificial intelligence (Oxford Insights 2017; Oliver Wyman Forum 2019a; Tortoise 2019b, 1). A closer look to the composition of the indicators shows how they converge in their focus on education. The AI indicators are particularly focused on innovation and economic competitiveness and, for example, the technology sector pillar of AIRI contains assessments of ‘innovation capacity’ and citizens’ skill set (human capital), building on data from WEF (GCI), the United Nations Educational, Scientific and Cultural Organization (UNESCO), Portulans Institute (Network Readiness Index and GII), as well as university rankings (QS and Scimago). The GCAIRI also contains metrics resembling the innovation ecosystem focusing on companies, workforce, 62

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Table 3.7: Composition of selected human capital, well-​being and AI indicators Better Life Indexa

Human Capital Indexb

Government AI Readiness Indexc

Global Cities AI Readiness Indexd

Global AI Indexe

Focus

Well-​being in terms of material living conditions and quality of life. Components:

Newborn’s expected amount of human capital by age 18. Components:

Governments’ preparedness to use AI in public services. Components:

Cities’ AI readiness. Components:

Countries’ capacity for AI. Components:

Material living conditions 1. Income and wealth 2. Jobs and earnings 3. Housing

63

Quality of life 4. Health status 5. Work and life 6. Education and skills 7. Social connections/​ community 8. Civic engagement and governance 9. Environmental quality 10. Personal security/​safety 11. Life satisfaction (subjective well-​being) Notes: a OECD, 2011– b World Bank, 2018– c Oxford Insights, 2017– d Oliver Wyman Forum, 2019 e Tortoise, 2019a

1. Survival 2. School 3. Health

Government 1. Vision 2. Governance and ethics 3. Digital capacity 4. Adaptability

Vision Implementation 1. Vision, priorities, mindset 1. Talent 2. Infrastructure Activation 3. Operating environment 2. Quality of life and diversity 3. Demographic enablers Innovation 4. Legal and 4. Research governmental enablers 5. Development

Asset base Investment Technology sector 5. Companies 6. Government strategy 5. Human capital 6. Workforce 7. Commercial ventures 6. Innovation capacity 7. Funding 7. Size 8. Education and research Data and infrastructure 9. Infrastructure 8. Infrastructure Development and trajectory 9. Data availability 10. Activation (development 10. Data over time) representativeness 11. Asset base (growth over time)

Human Capital and the Global Talent Competition

Index (producer)

Knowledge Alchemy

funding, education and research, and infrastructure. It also explicitly builds on the talent competition paradigm seeking to assess if there is ‘a reservoir of talent’ (Oliver Wyman Forum 2019b) in the cities analysed. The GCAIRI’s emphasis on quality of life also bears resemblance to the indicators of well-​ being and city rankings. The GAI exhibits great similarities conceptually with these indicators. The GAI contains sub-​pillars on ‘talent’, owing to the talent competition paradigm, as well as research and development sub-​ pillars, indicative of an innovation ecosystem perspective. Altogether, the global talent competition paradigm now also pervades the future-​oriented indicators of AI governance intent on measuring countries’ and cities’ AI readiness. We elaborate on how this formula has been translated in various policy domains and put into practice in the second half of the book. In the next section, we turn to the craftsmen of global indicators with special focus on talent competition.

Producers of global knowledge governance: the case of Institut Européen d’Administration des Affaires and the Global Talent Competitiveness Index Contrary to preconceptions, global knowledge governance and global data analytics are not the monopoly of large private American companies like Google or Facebook. European and Asian private actors, businesses, universities, think tanks and research institutes also play a key role. Since the beginning of the 2000s, this is clearly visible in the proliferation of global governance indices that are used to evaluate and plan human resource management and to foster innovation and competitiveness. Behind this development is the realization that humans are resources, and that countries and companies are engaged in a competition for skills and human capital (or ‘talent’). Some of the most influential tools are the World Bank’s WGI, the WEF’s annual Global Competitiveness Report and INSEAD’s GTCI. There is considerable synergy between these tools as they all seek to benchmark the performance of countries and economies. These and other indices and benchmarking reports structure the knowledge produced, leading to path dependencies of various forms and density, impacting how decision-​makers perceive the world and how they anticipate future developments. They are key elements in anticipatory innovation governance (OECD 2020a). In this section, we will have a closer look at the GTCI as an index and benchmarking tool, INSEAD, the institution that provides the necessary financial and organizational support from its production, and the individuals behind it. The GTCI is an index that provides a tool for public and private actors in their decision-​making about talent and human capital policies. In existence since 2013, the creators of the index are the Paris-​based private, 64

Human Capital and the Global Talent Competition

graduate-​only business school INSEAD, the French-​Swiss Adecco Group, and the Singaporean Human Capital Leadership Institute (HCLI). This was not a new venture for INSEAD. Since 2001, it had been collaborating with the WEF to publish the Global Information Technology Report, which was based on another index, the Network Readiness Index. Since 2007, INSEAD, in the persons of Soumitra Dutta and Bruno Lanvin, has been co-​producing the GII with Cornell University and the World Intellectual Property Organization (WIPO). The Zurich-​based Adecco Group is one of the world’s largest human resources providers and a temporary staffing firm. It provides services in temporary staffing, permanent placement, career transition and talent development, as well as business process outsourcing and consulting. It covers many sectors, including office, industrial, technical, financial and legal, among others. The link between Adecco and INSEAD is through the current chief executive officer (CEO) of Adecco: Alain Dehaze, a graduate of INSEAD like many other members of Adecco’s board. The HCLI is a subsidiary of Temasek Management Services, which is wholly owned by Temasek Holdings (Private) Limited, a Singaporean holding company owned by the Government of Singapore. It is supported by the Singapore Ministry of Manpower and the Singapore Economic Development Board. The HCLI seeks to develop leadership and strategic human capital management capabilities in Asia. TATA Communications is an Indian telecommunications company that is part of a larger, Indian multinational conglomerate manufacturer of automobiles, airplanes and other products. Current partners of the GTCI include Tsinghua University in China, Alliance Sorbonne University (for analysis of alliance universities see Kauppi 2022), and the Wharton School of the University of Pennsylvania. With a similar methodology, INSEAD also produces the Global City Talent Competitiveness Index, focusing on cities rather than countries. INSEAD plays a key role in the GTCI. It does not only house and finance the production of the index. INSEAD has over 60,000 alumni, and thus it also trains through its numerous study programmes the minds that are expected to occupy important positions in the global business world, and the current and future consumers of the data it produces.

The Global Talent Competitiveness Index The GTCI is a tool that resembles in many ways previous tools developed by INSEAD. Specifically, the Global Information Technology Index and the GII developed by Soumitra Dutta (INSEAD-​Cornell) and Bruno Lanvin (INSEAD). The 2013 GTCI composite index had four input variables (talent enablers, attraction, growth, retention) and two output variables (vocational 65

Knowledge Alchemy

training and global knowledge skills). Enablers include evaluations of the political and business landscape, attraction includes internal and external openness (such as for investments, social mobility), access to growth by formal education, opportunities and lifelong learning, and retention by sustainability and lifestyle. Output variables include vocational training, which includes improving employable skills and labour productivity, and global knowledge skills which are divided into higher skills and competencies and talent impact, pointing to skills that ‘the new global knowledge economy demands’ (INSEAD 2013, 19). These were measured with a mixture of hard, composite and qualitative data. Overall, the first GTCI consisted of three indices: 1. The Talent Competitiveness Input sub-​Index is the simple average of the first four pillars. 2. The Talent Competitiveness Output sub-​Index is the simple average of the last two pillars. 3. The Global Talent Competitiveness Index is the simple average of the six pillars. (INSEAD 2013, 200) The GTCI model included 48 variables, which fell within the following categories: (1) hard/​quantitative data (19 variables); (2) index/​composite indicator data (9 variables); and (3) survey/​qualitative data (20 variables). Hard/​quantitative data are based on sources from UNESCO, the United Nations Conference on Trade and Development (UNCTAD), International Labour Organization, World Bank, the OECD and the New York-​based, non-​profit business membership and research group organization, the Conference Board, the members of which cover most of the Fortune 500 companies. The sources of the index/​composite indicator data are the World Bank, INSEAD and WIPO, the Canadian conservative and libertarian public policy think tank the Fraser Institute, the British QS Intelligence Unit that produces its own university and management school indexes, Yale University and Columbia University, as well as the International Telecommunication Union. Finally, the survey/​qualitative data come from the WEF’s executive opinion survey and the London-​based think tank Legatum Institute’s Legatum Prosperity Index, which draws on the Gallup World Poll (INSEAD 2013, 200). In recent years, INSEAD and its Master of Business Administration (MBA) and Executive MBA (EMBA) programmes have evolved to become recognized as some of the best in the world, at least following the criteria used in global rankings (see Table 3.8). It has campuses in Singapore, Abu Dhabi and San Francisco. It was the first European business school to provide an MBA degree, and is a serious competitor to US and UK-​based global leaders like Harvard Business School and London Business School. 66

Human Capital and the Global Talent Competition

Table 3.8: INSEAD position in Financial Times global rankings 2016

2017

2018

2019

2020 2021

INSEAD MBA

1st

1st

2nd

3rd

4th

1st

Tsinghua-​INSEAD EMBA (TIEMBA)

2nd

3rd

3rd

9th

5th

5th

INSEAD Global EMBA (GEMBA)

4th

8th

13th

19th

9th

n/​a

The GTCI is an ‘innovative, annual benchmarking study’ (INSEAD 2021). It is the product of the joint effort with sponsors and knowledge partners from government, academia and industry, and it aims to give governments and businesses the distilled data from 125 countries needed to inform their decisions about talent policies and strategies. The report itself has details about methodology as well as country profiles (INSEAD 2021). In 2017, the European Commission’s Joint Research Centre (JRC) conducted a methodological analysis of the quality of the GTCI, concluding that: On the whole, the analysis of the correlations at the sub-​pillar level reveals that the statistical structure of the GTCI model is coherent with its conceptual framework, given that sub-​pillars correlate strongly with their respective pillars. Furthermore, all pillars correlate strongly and fairly evenly with GTCI itself, which indicates that the framework is well balanced. (Saisana et al 2017, 96) What is interesting about the JRC report is not so much that it wholly endorses the GTCI approach, but rather that the European Commission’s JRC now acts as an auditor for global rankings, which confirms the overlapping transnational networks between knowledge production and institutions, as well as private and public actors. This is confirmed by the following clarification by the authors of the 2013 GTCI: ‘The JRC has audited various index projects. The most recent ones include The Global Innovation Index (WIPO and INSEAD), environment Performance Index (Yale and Columbia), and Corruption Perceptions Index (Transparency International)’ (INSEAD 2013, 200). As Table 3.9 indicates, unsurprisingly, small European, export-​dependent economies do well in the index in 2020. Since 2013, the leading countries have been pretty much the same, leading to a growing gap between countries from North America, Europe and Asia, and countries from Africa and Latin America for instance. Looking at developments from 2013 to 2020 (INSEAD et al 2020), we can see that the ‘champions’ include the small Nordic and European countries like Switzerland and the Netherlands, the US and Canada, as well as Singapore. Movers include larger, developing 67

Knowledge Alchemy

Table 3.9: GTCI top ten ranked countries in 2020 Country

Overall rank

Switzerland

1

United States

2

Singapore

3

Sweden

4

Denmark

5

Netherlands

6

Finland

7

Luxembourg

8

Norway

9

Australia

10

Source: GTCI (2020)

countries like China, India and Russia, many of which are heavily investing in R&D. Laggards include countries like Hungary, Poland and Brazil, and limpers include Slovakia and Latvia. Addressing the idea of global talent in the age of AI, the GTCI 2020 innovated in several ways compared to the GTCI 2019. Up from 68 variables, it now measures 70 variables (including one of ‘technology adoption’) from 132 countries instead of the 125 in previous editions. Like in previous years, it measures national economies’ capacity to enable, attract, grow and retain talent, while also examining performance in vocational and knowledge skills. The 2020 edition is more comprehensive than previous editions as it represents now 98 per cent of the world’s GDP and 94 per cent of its population. In addition to the core partners, Adecco and INSEAD, there have been three different third partners (identified in italics): • 2020–​: INSEAD, Adecco Group and Google. • 2018–​2019: INSEAD, Adecco Group and TATA Communications. • 2013–​2017: INSEAD, Adecco Group and HCLI. It seems that knowledge on technological competitiveness had taken an ever-​ more important role when TATA Communications, a telecommunications firm that was described as a ‘high-​technology organization’, was chosen as a third partner after the founding partnership with HCLI (temporarily) ended. It was then followed by Google underlining its AI expertise. The original third producer HCLI still has a representation in the GTCI advisory board (INSEAD 2017).

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Human Capital and the Global Talent Competition

At the origin of the GTCI are two individuals who have played a key role in influencing INSEAD’s global knowledge strategy: Paul A.L. Evans, currently Emeritus Professor of Organizational Behaviour and the Shell Emeritus Chaired Professor of Human Resources and Organizational Development at INSEAD, and Bruno Lanvin, the current Executive Director for Global Indices at INSEAD. The Founding Academic Director of the GTCI (2013–​2018), Evans studied law at Cambridge, received an MBA at INSEAD, followed by a PhD in management and organizational psychology from MIT. He was recruited to INSEAD in 1974 as an assistant professor. Bruno Lanvin started his university studies in mathematics and physics at the University of Valenciennes, followed his studies with an MBA from INSEAD’s domestic competitor, Ecole des hautes études commerciales, is an alumnus of INSEAD’s International Directors Programme, and finalized his university studies with a PhD in economics from the University of Paris I, Panthéon-​Sorbonne. For more than 20 years with the United Nations, Lanvin worked in a number of capacities with UNCTAD, the UN ICT Task Force and the UN Department of International Economic and Social Affairs. The UN ICT Task Force seems to have been a key venue where actors from different sectors like government, business and NGOs were able to forge cooperation and common projects. From 2000 to 2007, he worked for the World Bank, where he was inter alia Senior Advisor for E-​strategies, and Regional Coordinator (Europe and Central Asia) for ICT and e-​government issues. He also headed the Capacity Building Practice of the World Bank’s Global ICT Department, and was Chairman of the Bank’s e-​Thematic Group. These years provided him with wide networks in business and politics, including with businessmen like member of the advisory board of the GTCI, Talal Abu-​Ghazaleh (Abu-​Ghazaleh 2021). From 2007 to 2012, he was the Executive Director of INSEAD’s eLab, managing INSEAD’s teams in Paris, Singapore and Abu Dhabi. In 2018, Bruno Lanvin became the President of the International Institute for Management Development, Lausanne’s Smart City Observatory and, in 2021, he became the director of the Portulans Institute, an independent nonprofit and nonpartisan research and educational institute based in Washington, DC. According to its mission statement, the Portulans Institute (2021) aims: • To develop cross-​c ommunity knowledge and dialogue on how people, technology and innovation contribute to sustainable and inclusive growth. • To inform policy-​makers, by producing independent, rigorous metrics and data-​based research.

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Knowledge Alchemy

• To collaborate with private sector leaders in driving a business agenda that invests in people, technology, and innovation for a prosperous common future. • To host and co-​organize events and conferences on the above issues affecting human-​centric sustainable economic prosperity. The Portulans Institute is thus hunting in the same grounds as INSEAD’s indices, providing knowledge-​based tools for decision-​makers on technology, innovation and global talent, and impacting global knowledge governance through its activities. One of the co-​founders of the Portulans Institute is Soumitra Dutta, who holds a PhD in computer science from the University of California in Berkeley and was professor at INSEAD before moving to Cornell, is extremely well connected as member of the Davos Circle, an association grouping long-​time participants to the meetings of the WEF in Davos, and on the boards of large companies Sodexo, a French food services and facilities management company, and Dassault Systèmes, a French software company. Overall, there is very little information available on Google’s role in the GTCI process, or more broadly on the division of labour/​contributions among partners in GTCI in general. On INSEAD’s webpage, Google’s role is said to be bringing business experience and perspective (INSEAD 2017). Most of the reporting on Google’s role is based on forewords in GTCI 2020 by Google Vice President (VP) Kent Walker. Not surprisingly, these findings mainly focus on AI that the compilers of the index chose as the theme for GTCI 2020. On Google’s side, AI had been for some time in the mind of Google CEO Pinchai as the next main area of development of the company and of computing more broadly, an area in which the company started to heavily invest with Google Brain and Translate (Lewis-​Kraus 2016). However, in a Google blog the same VP also addressed the role of AI for national competitiveness in other contexts, namely American politics (Walker 2021). Moreover, in these blog posts other knowledge production partnerships are discussed.2 In the few pieces of reporting (VOA News 2020; Wiggers 2020) referencing Google and GTCI, VP Walker is quoted as highlighting the role of AI and its opportunities as well as a need for all societal actors (government, trade unions and so on) to invest in it but also ‘shape a future that works for everyone’ (Wiggers 2020). Google VP Walker also stresses the need to ‘anticipate these changes and take steps to prepare for them’ in reference to labour markets (Wiggers 2020). In their reporting,

2

For example, Google-​National Science Foundation (USA) partnership on AI-​Human Interaction (Walker 2021) and AI for Social Good report with UNEP and Asian-​Pacific universities (Bathia 2021). 70

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VOA news also noted that Google itself had a massive AI capacity as it had collected vast amounts of data that were needed to develop algorithms (VOA News 2020). It is precisely with this massive database that Google was able to develop machine learning in areas like medical image recognition and translation (Chinese-​English-​Chinese).

The Global Talent Competitiveness Index’s advisory board The GTCI has an advisory board that mirrors some of the global networks that take part in global knowledge governance. It has been largely the same from the beginning. In 2013, Thierry Breton was originally listed as ‘CEO at Atos Origin [an IT firm] and former Minister of finance for France’ and Mats Karlsson was then at the World Bank (INSEAD 2013). Breton is a particularly prominent member of the board as he is currently the EU Commissioner for Internal Markets, including digital policies, defence and space, AI and cybersecurity. French President Emmanuel Macron chose him for that position in 2019 after Macron’s first choice, Sylvie Goulard, currently deputy governor of the Banque de France, had to give up her candidature because of financial scandals relative to the financing of her political party, MoDem. Macron had succeeded in negotiating an unusually broad portfolio for this position in the European Commission and thought Breton, who had been the CEO of large French tech companies like Thomson and France Télécom, would be a perfect candidate to replace Goulard. At the interface between government and business, Breton is an unusual member of the French business elite. Instead of going through the grandes écoles, he studied electrical engineering and made a career in various French ICT companies. Importantly, he is the first French grand patron (CEO of a large French company) to become European Commissioner, confirming the connivance between French business and President Macron’s European and global ambitions. Since 2019, and closely following Macron’s EU-​related political priorities (for example, digital sovereignty, AI), Breton has been pushing hard for the EU to develop competitive digital policies, calling for more European cooperation and funding in the areas of military and space equipment. Although Breton’s boss is formally speaking European Commission VP Margrethe Versteger, he has not hesitated to disagree with her, for instance, on the aborted merger between Siemens and Alstom. A second key member of the GTCI’s advisory board is Mats Karlsson, former VP of the World Bank, and director of the Swedish Institute of International Affairs from 2014 to 2019. As the VP for external affairs, Karlsson possessed considerable social capital in global business and politics. Other board members include three professors of management, from the US (Peter Cappelli from Wharton), Singapore (Arnoud de Meyer, emeritus 71

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professor at the Lee Kong Chian School of Business of Singapore Management University) and Japan (Yoko Ishikura, Hitotsubashi University), as well as (vice-​)chairmen from an Indian technology company (Vineet Nayar, HCL Technologies) and a Middle-​Eastern company focusing on education (Talal Abu-​Ghazaleh, Talal Abu-​Ghazaleh Organization) (INSEAD, 2021). Ishikura was a former McKinsey manager. De Meyer is also a key figure, as he is the founding dean of INSEAD’s Asia campus in Singapore, former director of the Judge Business School in Cambridge, and a current member of the board of directors of INSEAD. The members of the board are part of the same transnational networks that fuse business academia (management and business schools), government agencies and ministries, and business (especially high tech). They circulated in mainstream or conservative public and private institutions like INSEAD, Ecole des hautes études commerciales, the International Institute for Management Development, Lausanne, Wharton, Singapore Management University, the European Commission and its Joint Research Center, OECD, UN agencies, the WEF and the World Bank. For instance, Peter Cappelli from Wharton was a member of the Distinguished Visitor Board, Ministry of Manpower, Singapore (2008–​2012), a member of the Human Resources Steering Committee, OECD Paris, a member of the Global Agenda Council on Employment for the World Economic Forum (2012–​2014), and a visiting scholar at Singapore Management University. While Paul Evans has a more academic profile, he has been very involved with organizing seminars and advising companies in human resources management. Similarly, Lanvin’s networks are extensive, covering international organizations and business schools in Europe, North America and Asia. Lanvin brought with him to the Portulans Institute three younger consultants from INSEAD’s GTCI team, Michael Bratt, an economist with a PhD from the University of Geneva, Rafael Escalona Reynoso, a PhD in regional planning from Cornell, and Anna Henry, who has an MBA from the University of Geneva. These actors have an educational background in business schools in France, Switzerland, Singapore, the UK and the US, and knowledge capital in areas like business management, e-​government, talent/​HR, innovation policy, AI and high tech. They are members of a transnational network of powerful groups (Kauppi and Madsen 2013) involved in global knowledge governance.

Conclusions In this chapter, we have discussed innovation rankings and city-​level measurements that now also link to policy indicators of AI. These metrics have come to draw heavily from previously published datasets, echoing the hegemonic views and ideological underpinnings already present in the field. The sharing of data is part of the evolving conventional power of global 72

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data production. Our empirical analysis focused the idea of ‘global talent competition’, where countries and cities are now competing over talented individuals, linked with world-​class educational and innovation systems. Here the field development of rankings has major implications for the global policies on education, innovation and AI as the major ranking producers are revising their indicators to analyse the social transformations anticipated through digitization and automation. Global policy scripts define specific but generalizable measures to address a policy issue, prescribing action. We conclude that the generalizability and predetermination of policy measures, as well as their prescriptive nature, has intensified through the use of indicators that now allow merging of data from different policy domains, linking also policy scripts of competitiveness, good governance, innovation and urban governance. This is now apparent in the crafting of policies on ‘global talent competition’ and the actors involved.

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PART II

Scripts, Imaginaries and Policy

4

Global Imaginaries of Knowledge Governance Introduction This chapter begins the second part of our book and turns to the scripts and imaginaries of knowledge governance to show how they shape diverse sectoral policies and institutional practices through numerical global scripts and formulas. As discussed in Chapter 3, global indicators bring coherence to transnational governance by providing decision-​makers with numerical global scripts to succeed in global economic competition. This is most apparent with the ‘world-​class university’ model (see Chapter 2) that now steers the higher education policies of most countries (Mittelman 2017; Rider et al 2020). We also discussed a new emerging script of ‘talent competition’ that builds on the earlier ideas of competitiveness, excellence in higher education and good governance (Chapter 3). To become effective, however, numerical knowledge needs to be narrated and communicated. Numbers alone are meaningless without the broader context and the interpretation of what these numbers symbolize. As discussed in our introductory chapter, policy scripts describe predetermined sequences of events based on storylines (Schank and Abelson 1977). While these are increasingly expressed in numbers (that is, digitization), we also see actors referring to different imaginaries of knowledge governance. These imaginaries are linked to grand narratives of global megatrends, pointing to intensifying global economic competition through digitalization and innovation, as well as the implications for those countries, institutions or individuals left behind. The ranking producers have also identified automation as one of their observed megatrends, discussing it as the ‘fourth industrial revolution’ or ‘second machine age’, where digitalization and automation ultimately affect global competitiveness, innovation and knowledge governance. 77

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Going beyond the state as a unitary actor, innovation and knowledge creation are tightly linked to cities as innovation hubs, reflecting the global trend of urbanization as part of the broader modernization movement. ‘Talents’ are important in this storyline. Policy makers, businesses and institutional leaders compete for the ‘best and brightest’ who would add value to their policies, product and service offerings. The talent competition script is one that builds on ‘more and more talents’ (attraction) and not on how to best integrate and retain this human capital over time, but this is also now changing. The storyline put forward by expert organizations such as the WEF is vague, but interestingly there are keen references to historical past that provide seeming analogies to the future. This chapter argues and shows that transnational knowledge governance operates on the perceptions of futures. We observe global imaginaries of competitiveness in knowledge governance, where countries –​and increasingly cities –​are being ranked, compared and identified as forerunners and laggards in innovation and digitization, and where automation through AI appears as a source of anxiety and future disruptions (cf O’Donovan 2020). We analyse the imaginaries produced by global ranking producers, who are major knowledge brokers on global competitiveness. We argue that the future narratives carrying the global imaginaries of competition paradoxically stress historical continuity and invented traditions, delimiting agency and alternatives for the future. Along with numerical objectification (see Chapters 2 and 3), the narrative elements of policy scripts are equally important parts of imagined global gold standards, and universal symbolic formulas of knowledge governance upon which knowledge alchemy is based. We will also discuss the OECD’s Anticipatory Innovation Governance initiative, launched in 2020, which aims to challenge established governance approaches by focusing explicitly on the future and foreseeable consequences of current action. While it acknowledges the plurality in claims for possible future, it has limitations in recognizing the politics and agency behind the claims for possible futures.

Global rankings and imaginaries of competitiveness, human capital and artificial intelligence Global ranking producers such as the WEF, as noted, have identified automation through digitization and AI as one of their observed megatrends, reflected in the rankings of economic competitiveness and innovation. These knowledge producers are further highlighting urbanization as a concurring global trend. Consequently, innovation rankings as well as regional and city-​level indicators have emerged, and they now position institutions and practices of national knowledge production at the heart of economic competitiveness. 78

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The rankings and indicators increasingly function as navigating tools for the uncertain future, tapping into the imaginaries of competitiveness amid technological changes through digitization and automation. Through objectification (Desrosières 1998), indicators help to define the scope of concepts such as ‘excellence’, ‘skills’ or ‘talent’, turning them into governable objects (Miller and Rose 1990). But equally important, the mechanism through which rankings obtain their governing effects is subjectification, where subjects of measurements are incentivized or pressured to acquire particular identities linked to proposed action (Erkkilä and Piironen 2018, ­chapter 2; cf Lawler 2014, 6, 69). Global indicators imply identities for ranked entities as citizens of a country or university employees, now engaged in global competition. Actors assume roles in narratives on ‘our’ economic competitiveness or ‘European path’ in AI. The importance of global policy scripts should be emphasized because they define specific but generalizable measures to address a policy issue (Kentikelenis and Seabrooke 2017). Indeed, unlike descriptive policy positions, policy scripts prescribe action based on storylines that spell out predetermined sequences of events (Schank and Abelson 1977). As we have argued in Chapter 2, the increasing use of rankings, as well as the conceptual and methodological convergence of the metrics, now allows actors to combine data from different policy domains, compare and evaluate performance. This development has also enabled actors to numerically propose generalizable ‘models’ for countries and cities as subjects of these measurements. In so doing, these numbers not only describe states of affairs, but they also prescribe policy models that are assumed to be more holistic. A storyline is instrumental in communicating the sequences of the policy scripts. For our purposes, the contemporary phase of knowledge governance is presented as a natural evolution of developments. Our analysis of policy discourse on talent competition identifies historical narratives that are used to communicate the policy scripts on knowledge governance, concerning their logical elements and actors, as well as normative and causal beliefs. This also involves new policy concepts such as ‘talent’. According to Reinhardt Koselleck, as the distance between the horizon of (future) expectations (Erwartungshorizont) and the space of (past) experience (Erfahrungsraum) grows, conceptual shifts are likely to occur, but also new concepts may arise to fill the gaps (Koselleck 2004). This also links to imagination as a category of history between the experienced and the expected (Schinkel 2005). Hence, the stories that the global knowledge brokers tell us about past experiences also project expectations for the future. Analysing the documents of the major ranking producers, we observe a discourse on ‘global talent competitiveness’ where the cognitive and normative aspects of this discourse are presented somewhat differently (cf Schmidt 2010). While the cognitive aspects of this highlight the 79

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skills, conditions and regulatory environment required for allowing the Schumpeterian creative destruction amid the rise of automation and algorithmic governance, the normative aspects talk about sustainable welfare and maintaining social cohesion facing such potential future disruptions. The future appears controllable, a competition of equal entities, navigated with the help of rankings that identify policy issues and institutional practices to improve. The imaginaries, narratives and discourses of competitiveness also imply temporal elements of governance. Remarkable in the discourse on digitization and AI is the uncertainty of its nature. Interestingly, there are keen references to historical past that provide seeming analogies for the future. We argue that to understand how global policy scripts, and their local variants, are being drafted, historical narratives are important in uncovering the underlying imaginaries; how they are being evoked and what redescriptions of the ideas are involved (Koselleck 2004). In particular, when describing history as continuity, process or progress, the narratives limit or even remove the horizon for alternatives (Arendt 1973, 137, 143; Owens 2017). The critical inquiry and contestation of such process frames mark an opening for political alternatives and agency (cf Palonen 2003; Hyvönen 2016). This part of the book further analyses how the imaginaries of global competition now draw from historical narratives and invented traditions (Hobsbawm 1987) and how policy change is incited with the help of political concepts (Skinner 1969; 1989), and what agency is implied in these scripts. We analyse the spatial and temporal aspects of the policy scripts on innovation and AI and their underlying imaginaries of competitiveness: what kind of future(s) do they talk about, what is the assumed past, what kind of developments and changes are expected, and where do they occur? How do historical narratives limit the horizons for possible future(s)? What kind of actions are necessary and legitimate based on these narratives and what kind of agency and role for government is perceived in these scripts? Our data selection covers the reports by the producers of the GCI, GII and GTCI datasets, which are prominent indicators of competitiveness, innovation and human capital (cf Global Innovation Index 2019; Lanvin and Monteiro 2019; World Economic Forum 2019c). Additional commentaries are derived from the World Bank’s report on changing nature of work launching the Bank’s new HCI (World Bank 2019, vii). The index producers show a rather positive assessments of the future, entailing significant continuity of practices even amid great transformations owing to automation and AI. The key to this continual progress, or evolution, is human capital, or talent, that is to be harnessed for economic competitiveness (see following chapters). Regulation of algorithmic governance is less pronounced. Altogether, we see a historical narrativity that projects past events into the future. 80

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Global indicators objectify and render measured entities open for policy interventions. They also fabricate global imaginaries that help to subjectify actors in the pursuit of desired results. The imaginaries and assumed identities are closely linked to policy scripts, associated with diffusion of policy ideas and organizational forms in science, education, individual rights and economic liberalism (Meyer et al 1997; Schofer and Meyer 2005; Simmons et al 2006; Drori et al 2009; Koo and Ramirez 2009; Halliday et al 2010; Pinheiro and Hauge 2014). There are currently converging global scripts on competitiveness, good governance, education and human capital, coming together with the help of rankings (Erkkilä and Piironen 2018, 231). We will next discuss what we perceive to be the four logical elements in the narratives that appear in the strategies and reports of global knowledge brokers, such as the World Bank, WEF, and INSEAD accompanied by Google: revolution; talent; regulation; and open society and urbanity. These four elements tell us a story about how to imagine the future, one in which the entities are engaged in a global competition. There would be great social turmoil due to automation and AI, and the role of government is to alleviate the pain of technological change through social policies and ‘reskilling’ rather than regulating the technological innovators and AI. The perceived success factors are also clearly stated. In order to remain competitive, countries need to focus on their ability to ‘attract and retain’ talent (that is, an educated and skilled workforce), but much of the regulatory emphasis has been on attracting rather than retaining. Innovation in the new technologically driven society is increasingly seen to take place in urban environments that also fulfil the virtues of liberal ‘open societies’. Interestingly, in the reports analysed, references to the future are riddled with narratives about the past, where historical events are evoked with seeming analogies to the assumed future developments. As we will discuss in greater detail in Chapter 5, this is clearly observable in the case of academic mobility.

‘The revolution’: knowledge governance, digitization and automation Institutional scholars and conceptual historians have drawn attention to crisis and junctures of governance as contexts where changes in ideas and ‘revolutions’ in worldviews occur, leading potentially to changes in institutional practices (Koselleck 2004; Schmidt 2010). Also external shocks such as economic crises and technological innovations are likely sources of institutional change (Krasner 1984; Mahoney 2000). At present, the organizations crafting global models and formulas of economic competitiveness are very much perceiving the world through the analogy of revolution, and the ranking producers identify automation through AI and algorithmic governance as one of their observed megatrends. 81

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The ‘changing nature of work’ (World Bank 2019) through the ‘fourth industrial revolution’ (World Economic Forum 2016a, 51) or ‘second machine age’ (INSEAD 2016, c­ hapter 3) is seen to revolutionize work and cause major shifts in wealth and class structures. In these future scenarios, ‘digitization’ and ‘globalization’ are discussed somewhat interchangeably (for example, Lanvin and Monteiro 2019, 8). An intertwined set of future challenges in competitiveness, equality and sustainability is identified by the WEF with parallel challenges of eroded social cohesion and climate change (World Economic Forum 2019c, 5, 9, 25). Yet, the path to the future is described positively as a historical continuum of mankind’s attempts to improve human conditions through agricultural and industrial ‘revolutions’ (World Economic Forum 2019c, 25). Automation through AI is linked to potential job and wage losses, but the history of telegraph lines is presented to reassure us that the technology to displace the ‘Pony Express’ eventually leads to increased prosperity: Technological change has traditionally been accompanied by fundamental societal changes, often including massive job losses. Historically, for instance, with the completion of the first US transcontinental telegraph line in 1861, the services of Pony Express riders became obsolete. Telegraph lines, however, soon became the basic fundament for the emergence of the new telecommunication industry, creating myriad new jobs over time. (World Economic Forum 2019a, 6; emphasis added) A complementary report to the GTCI yearbook presents a history of ‘thinking machines’, dating back to ancient Egyptians and Greeks, celebrating the efforts of mathematicians such as Alan Turing, and also citing the backlashes towards early AI, now overcome (Tata Communication 2018, 9, 15). The World Bank explores a set of historical cases that provide seeming analogies for the future challenges (World Bank 2019). These range, for example, from the works of Adam Smith on human capital and Rousseau’s social contract to the spinning jenny and Otto von Bismarck’s social insurance (World Bank 2019, 20–​22, 50, 124). Yet there are very few concrete examples of the technological innovations of tomorrow in the reports, showing the difficulty of imagining the future. According to the World Bank, there are also geopolitical and developmental divisions in countries’ abilities to respond to these technological changes and profit from them, owing to countries’ education, wealth and developmental level and population structure (age) (Christensen et al 2018, 1, 3; World Bank 2019, vii, 3, 20, 22, 71–​72, 92). A historical narrative on the industrialization of weaving is produced to anticipate the future changes of automation, providing a sense of conformity, but also alarming the governments that are not investing enough in human capital; 82

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the Bank’s launching of the HCI is presented as a ‘fix’ for this problem, as the following quote shows. ‘Machines are coming to take our jobs’ has been a concern for hundreds of years –​at least since the industrialization of weaving in the early 18th century, which raised productivity and also fears that thousands of workers would be thrown out on the streets. Innovation and technological progress have caused disruption, but they have created more prosperity than they have destroyed. Yet today, we are riding a new wave of uncertainty as the pace of innovation continues to accelerate and technology affects every part of our lives. We know that robots are taking over thousands of routine tasks and will eliminate many low-​skill jobs in advanced economies and developing countries. At the same time, technology is creating opportunities, paving the way for new and altered jobs, increasing productivity, and improving the delivery of public services. When we consider the scope of the challenge to prepare for the future of work, it is important to understand that many children currently in primary school will work in jobs as adults that do not even exist today. That is why this Report emphasizes the primacy of human capital in meeting a challenge that, by its very definition, resists simple and prescriptive solutions. … Innovation will continue to accelerate, but developing countries will need to take rapid action to ensure they can compete in the economy of the future. They will have to invest in their people with a fierce sense of urgency –​especially in health and education, which are the building blocks of human capital –​to harness the benefits of technology and to blunt its worst disruptions. But right now too many countries are not making these critical investments. Our Human Capital Project aims to fix that. This study unveils our new Human Capital Index, which measures the consequences of neglecting investments in human capital in terms of the lost productivity of the next generation of workers. (World Bank 2019, vii; emphasis added) This demonstrates how historical narratives carry ontological premises, causal beliefs and policy scripts linked to indicators as tools for navigating the future. Overall, numerical governance has got temporal implications through its precautionary or pre-​emptive motivations (Dahler-​Larsen 2014; Hansen 2015; Johns 2016). As tools of economic order, indicators seemingly help to reduce risks, uncertainties and complexity. In addition, the prestige linked to the indicators contains a temporal element, entailing promises for the future. According to the World Bank, digitization and automation are to cause major social changes that highlight human capital and IT skills, opening geopolitical and developmental divisions in countries’ ability to respond to these technological changes and profit from them: the age of population, 83

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countries’ wealth and developmental level, and level of education (Christensen et al 2018, 1, 3; World Bank 2019, vii, 3, 20, 22, 71–​72, 92). The concern over human capital is also an explicit motivation for the World Bank’s launching of the HCI in 2018 (World Bank 2018) –​while digitization and technological development present challenges that ‘resist simple and prescriptive solutions’ (World Bank 2019, vii), a holistic analysis through indicators is seen as a remedy for countries’ insufficient investments in ‘human capital’ (cf Chapter 2).

The race for talent: open society and urbanity In the reports, human capital is often reduced to ‘talent’, ‘education’ and ‘skills’ (that is, skilled individuals) that countries are competing over in the ‘global talent competition’, where talent is to be ‘grown’, ‘attracted’ and ‘retained’ (INSEAD 2016, 6). Both cognitive and socio-​emotional ‘skills’ that are ‘transferable’ are highlighted over sectoral knowledge (Christensen et al 2018, 6; Lanvin and Monteiro 2019, 39; World Bank 2019, 7, 70; INSEAD et al 2020, 5), to be learned already in early education, throughout formal education, and lifelong learning (Christensen et al 2018, 6; World Bank 2019, 10, 72). In these policy scenarios, higher education institutions have a clear role to play as the key knowledge institution in teaching, training and retraining future and current manpower for the labour market. The reports maintain that skills that are needed cannot exhaustively be learned at school, yet the educational systems and innovation environments are highlighted as keys for success in the context of ‘automation’, ‘digitization’ and ‘globalization’. Moreover, global competitiveness is understood to be conditioned by countries’ or cities’ ability to attract ‘entrepreneurial talent’, essential for firms, nations and cities alike (Lanvin and Monteiro 2019, 5), or ‘talent adaptability’ (World Economic Forum 2019c, 8; cf World Bank 2019, 10, 70). Recent analysis of global governance, international political economy and geography have highlighted the rise of cities and regions as subnational actors (Moisio 2008; Sassen 2008), allowing also to imagine the displacement of the nation-​state in global governance (Archer 2012). This is also reflected in the reports by the global indicator producers that identify cities and small states engaging in a race for human capital or ‘talent’ as a key element of economic competitiveness in the context of rapid technological changes and political and social disruptions implied by the changing nature of work (World Economic Forum 2014; World Bank 2015; World Economic Forum 2016b; Global Innovation Index 2019, 22, 123; Lanvin and Monteiro 2019; World Bank 2019; INSEAD et al 2020, 91–​93). Also algorithmic governance and AI are tightly linked to cities that are seen to be best positioned to face the challenges and opportunities of the automated future as its testbeds 84

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(INSEAD et al 2020, 91–​92). As we will see in Chapter 6, there is now a keen reference to the ideational models put forward by rankings, particularly concerning innovation, talent, urban governance and competitiveness. The measurements pose a challenge to the state-​centric understanding of global governance as urban areas are represented as hotbeds of innovation. The switch of focus from country level to city level is ideationally justified by resorting to historical narratives, references to past thinkers and eras, and the evidence of grand global trends. Analysing the reports of central ranking producers in innovation and competitiveness, such as the WEF, we see subjectification through historical narratives on city-​states explaining the observed changes and future of urban governance, idealizing a Medici-​era Florence, where skilled workers and their apprentices were moving freely between cities: For most people, the map of the global economy that comes to mind is of nation states interconnected through flows of trade, capital, people and technology. However, before the ascendancy of the Westphalian nation state in 1648, the primary political, economic and cultural unit was the city. An alternative map of the global economy comes to mind: one of cities connected across land borders, seas and oceans through the exchange of goods and services, foreign direct investment, migrant and short-​term workers, and border-​hopping technology. (World Economic Forum 2014, 7; emphasis added) The references to such invented traditions provide seeming help for navigating the uncertain future (Hobsbawm 1987). Conceptually, urbanization is moved to the conceptual field of economy and technology, conflated with (global) competition and digitization (Koselleck 2004; cf Kangas 2017). Furthermore, urbanity is strongly associated with openness, referring to liberalism and ‘open’ innovation environments, but openness also carries connotations of economic openness and mobility in the free movement of individuals. For example, in its work on the competitiveness of cities, the WEF refers to education under the label of soft connectivity. Understood broadly as social capital, another element of soft connectivity is the Popperian ‘open society’ that finds a historical reference point in the pre-​modern era (World Economic Forum 2014, 13). This historical narrative now encompasses the ideas of institutional economics and market transparency (cf Rodrik 1998; Stiglitz 1998). [Soft connectivity] concerns an atmosphere of tolerance, free expression and cosmopolitanism, all characteristics of what the philosopher Sir Karl Popper called the ‘open society’. Today, they are highly conducive to the generation and dissemination of ideas, and to entrepreneurship, 85

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innovation and economic growth, just as they were in cities at the heart of the pre-​modern European and Asian ‘miracles’. (World Economic Forum 2014, 13) Openness is also associated with access to data and efficiency of government, seen as part of a ‘platform era’ of innovative city ecosystems or ‘smart cities’ (Global Innovation Index 2019, 123; INSEAD et al 2020, 92, 93, 102). Here openness refers to states’ strategies to spur innovation through exploitation of public sector information and simplified issuance of residence permits for highly qualified workers to compete over ‘talent’ (Global Innovation Index 2019, 22). Use of history is part of subjectification, linking identities to proposed action. Historical narratives provide a sense of orientation and continuity, but they simultaneously fix the political imaginaries, portraying a world of economic competition with little alternatives for policy. Agency is posited on individuals, businesses and governments, the ‘grand designers’ of their destinies (Lanvin and Monteiro 2019, 39), but only as economic actors. We stand on the cusp of a world of work that will enable individuals to develop and evolve in ways never imagined before –​to follow their passions and not just follow the herd. It’s a world where businesses can unleash new levels of productivity and competitiveness. And it’s a world where governments can future-​proof their economies and unlock untapped prosperity. In a future that works for everyone, we will be not just chameleons, but grand designers of our own destinies. (Lanvin and Monteiro 2019, 39) The atomist perception of competition sheds responsibility to all actors concerned. Seen through the natural scientific analogy of ecosystem, governments are to foster AI-​driven innovation and alleviate its pain.

Regulation and role of government In the reports by ranking producers, the role of the government is reduced to driver of reskilling, addressing the demands from both citizens and businesses. As platform economy makes the wealth distribution unequal, governments are called upon to invest in human capital (World Economic Forum 2017, 4; 2019b, 10; World Bank 2019, 9, 10, 19). Regulation should be innovation-​friendly, meaning curbing the policies that might discourage or dampen the speed of job creation and innovation (World Bank 2019, 29, 31). In addition to the content of the policies, their sequencing is also stressed (World Economic Forum 2019c, 5), typical for policy scripts as knowledge structures (cf Schank and Abelson 1977). 86

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Investments for social protection are needed, also to maintain economic growth amid less structured working careers, the gig economy and joblessness (World Bank 2019, 9, 19, 20, 53–​54). Innovation ecosystems thinking is firmly present in the reports, seeing innovative firms as disruptors and governments as the actors responsible for promoting the ‘ecosystem’ (World Economic Forum 2019b, 10). The government has the double task of regulating and fostering the disruptive ‘superstar’ companies (World Bank 2019, 12). As for regulation, the WEF argues for a mix of governance tools and tactics that highlight standardization over international legislation and conventions; the global governance of AI should consider the ‘diversity of values’, though the EU’s Charter of Fundamental Rights and the UN Declaration of Human Rights are mentioned as examples (World Economic Forum 2019a, 9, 12, 15). The World Bank and WEF argue that global agreements on digital taxation and anti-​trust policies are needed, allowing also revenues for investments in social protection, wellbeing and human capital (World Bank 2019, 9; World Economic Forum 2019c, 9). Yet, the WEF sees geopolitics and global governance as under threat (World Economic Forum 2019c, 5), while seeing today’s competitive economies as having a smooth transformation to the digital future (World Economic Forum 2019c, 28). Here the rankings again function as tools of prediction and as essential to governance.

Imaginaries and limits of policy scripts The imaginaries of competition at play in the cases analysed draw from different modalities of competition: economical, natural-​scientific and geopolitical. Nevertheless, the different analogies of competition are now all firmly linked to the semantic field of economy (cf Koselleck 2004). The perception of agency is very limited. The baseline narrative of ranking producers sees competition as an economic activity that takes place between atomist entities that are out there to compete with one another, be they countries, innovation hubs, academic institutions or individuals, ignoring the tremendous interdependence between them. The innovation ecosystem thinking additionally draws from a natural scientific analogy, where the disruptors (firms) are causing inevitable innovative destruction in a Schumpeterian sense. The ‘function’ of government in this ‘ecosystem’ is twofold and paradoxical: to alleviate the effects of disruption and to foster new disruptors (innovators), ultimately leading to continuous cycles of disruption. There is a high premium placed on mobility that assumes strong continuity of global networks and flows of ideas and individuals, or even their intensification. This shows the persistent difficulty of imagining a post-​carbon 87

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society (cf Hajer and Versteeg 2019), or perceiving potential limits for the global diffusion of liberalism and its link to innovation (cf Roberto and Mounk 2019). While policies on knowledge economy and AI highlight innovation, human capital and collective knowledge production, the rise of automation also questions its very premises, marking also a potential limit of the growth theory (O’Donovan 2020). The high expectations and generally positive connotations loaded on the concepts of automation and AI are problematic, as AI runs the risk of becoming a ‘third-​term’ (cf Jessop 1998) that allows us to bypass dichotomies such as democracy–​efficiency, public–​private and market–​hierarchy. Overall, the imaginaries and historical narratives of competition, innovation and AI discussed in this chapter come to depoliticize the implied policy scripts and limit alternatives. Instead of perceiving history as continuity, process or progress, we should acknowledge its fragmentary character and also the ability of actors to change its course (Arendt 1973, 137, 143; Owens 2017). Important here is the possibility to question the historical narratives and their implied ontologies and to see beyond them. Indeed, should rankings producers and international organizations be the sole authorities of narratives of history? Or should others with lived experiences be allowed to contribute to shaping these narratives? This would imply politicization of such process frames (Palonen 2003; Hyvönen 2016), opening a horizon for alternative future(s) and agency at present. Already the global outbreak of the COVID-​19 pandemic has questioned dramatically the premises of European and global approaches on human capital, and AI (see Chapter 6). With global economy and mobility coming to a halt and the ‘open society’ placed under lockdown, the knowledge sector has also been hit hard and its activities curtailed and disrupted (Grove 2020; Ross 2020). These developments show how the imaginaries and premises underlying policy scripts can change rapidly at times of crisis (cf Peters et al 2005; Robertson 2017, 31). For just as the historical narratives on innovation and competitiveness may captivate us on sharing a political imaginary, the debunking of these narratives can open new horizons for action, with different perceptions of ontology, agency and alternatives for policy.

From reaction to anticipation: the OECD’s anticipatory innovation governance In 2020, the OECD launched a new type of approach to governance that challenges established governance approaches: anticipatory innovation governance (AIG). The novelty of anticipatory governance is that the focus is explicitly on the future and foreseeable consequences of current action. Some of AIG’s antecedents are, on the one hand, Alvin Toffler’s pioneering book Future Shock (1970) and his writing about anticipatory democracy 88

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(1975), and, on the other hand, a series of studies developed at the Centre for Nanotechnology in Society at Arizona State University starting from the beginning of the 2000s (Barben et al 2008; Guston 2014). There has also been a recent interest towards anticipatory governance among the scholars of global governance and education (Berten and Kranke 2022; Robertson 2022). In his pioneering work, Toffler framed the question of the future in terms of the combination of citizen participation with future consciousness. For him, technology was a deeply political problem that needed to be addressed in democratic ways by increasing participatory democracy and finding ways to tackle technological challenges democratically. By contrast, the focus of Barben et al is on trying to foresee the spreading of an innovation’s effects (Fuller 2010, 533) with a veneer of public engagement. According to Barben et al (2008, 991–​992), anticipatory governance ‘implies that effective action is based on more than sound analytical capacities and relevant empirical knowledge. It also emerges out of a distributed collection of social and epistemological capacities, including collective self-​criticism, imagination, and the disposition to learn from trial and error’. This definition tells us that reflexivity and a variety of voices in society are considered, and thus anticipatory governance provides a more humane version of technoscience (Guston 2014, 242). Guston (2014, 218) defines anticipatory governance as ‘broad-​based capacity extended through society that can act on a variety of inputs to manage emerging knowledge-​based technologies while such management is still possible’. The focus is on upstream engagement of emergent, that is not yet concrete, technologies such as nanotechnologies while emphasizing the importance of public deliberation (Barben et al 2008, 986) and ‘small voices of folks’ (Guston 2014) that would add a human dimension to technoscience. Not everyone is so optimistic (Fuller 2009, 209). The danger is that by creating new technologies of power, such as focus groups, science cafes, interactive platforms and citizen gatherings, anticipatory governance will become just a tool that will ease the acceptance of new technologies and legitimize the interests of big tech. The reasons for recent growing Interest in AIG (OECD 2020a) are numerous, ranging from climate change and COVID-​19 to more unstable geopolitics (for example, China and Russia). Behind these we find an increase in the policy value of the future as a political tool where foreseeable consequences of public action attempt to take the place of the traditional unintended consequences of public action. As the author of the OECD’s report explains: First, why does government need better ‘anticipation’? The answer is simple. The nature of the issues that governments are confronted with today is volatile, uncertain, complex, and ambiguous. Think of global security threats, pandemics, technology development, or ageing societies. Governments cannot stand still and wait for things to happen 89

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to them –​they need to prepare for what is coming next, continuously identify, test, and implement innovative solutions to benefit from future opportunities, while reducing the risks through increased resilience of their public systems. This practice is what we have come to refer to as ‘anticipatory innovation’. (Tõnurist 2021) AIG combines three dimensions: foresight and weak signals (anticipation); new techniques and tools of governance (innovation); and government’s ‘ideal futures’ (governance). Anticipation is defined as ‘the act of creating knowledge about an uncertain future’ (Tõnurist 2021, 7; see also Katzenstein and Seybert 2018). In Tõnurist’s words, anticipatory innovation enables influencing socio-​technical shifts by starting to realize those visions of the future today. This proactive action requires new ways of recognizing early signals of change and facing knowledge and ontological uncertainty (Tõnurist 2021, 13). According to some observers, this reorientation requires not only redefining what we see but how we see the world and its future (Scoblic and Tetlock 2020). The OECD’s AIG project is part of a broader OECD-​led endeavour that aims to develop a new public sector innovation model. Spearheaded by an informal innovation governance expert group consisting of the Finnish Ministry of Finance, the Swedish Vinnova innovation agency, the European Commission’s Joint Research Center and the London School of Economics’ Capstone project, it was drafted while the COVID-​19 pandemic had just started in 2019–​2020 and published on 24 December 2020. The report focuses heavily on technological change that seems to be imposed on societies, autonomous vehicles, AI, biotechnology and ‘health considerations’ (Tõnurist and Hanson 2020, 7) from a technocratic and apolitical point of view. AIG is presented as a purely organizational problem that aims at a holistic governance of society. From a broader perspective, all kinds of governance such as collaborative governance contain a dimension of anticipation. Some have argued that anticipation is the necessary condition for any kind of social action (Poli 2017), and fundamentally ‘every policy is a prediction’ (Scoblic and Tetlock 2020) in the sense that it contains guidelines for future action. Until now, anticipation was constructed into various governance models in an implicit way. What is new with the OECD’s anticipatory governance is that anticipation is formulated explicitly as the key element of governance.

Anticipatory governance and the politics of future Anticipatory governance could be defined as governance action that, on the basis of previous action, takes into account, anticipates, expects or forestalls later actions. In other words, present action is dependent on future 90

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circumstances (Rosen 1985), or rather on the representations of future circumstances. Anticipatory governance involves events, objects and ‘things’ in the world. The ‘solution’ to the openness and unpredictability of the future is to switch from a reactive modality of action (adaptation, regulation, coordination, collaboration) (Rosen 1985, viii) to a ‘readiness modality’ that anticipates or simulates certain future developments. With AI and big data, a sense of control of the future is created. The crucial questions are which future events are anticipated and who determines these –​the very essence of politics. Indeed, this points to the political nature of anticipated future and the ability to open new alternative horizons and politicize certain domains of social life whose political nature has not yet been acknowledged (Palonen 2003). Until recently, reactive behaviour and reactive governance have been dominant, and there was little discussion of anticipatory action except in public policy documents relative to government planning or economic studies on futures and economic development (of unemployment, the value of stocks, and so on). A paradigm shift in governance research is based on the argument that anticipation has priority over reactive action (Poli 2017). Anticipation challenges established ideas concerning social action and temporality. The concept of anticipatory governance has numerous consequences for the analysis of human agency and governance. For instance, in contrast to the most common theories of the state (bureaucratic, legal, Marxist, postmodern), which maintain that it is a set of institutions and practices, from an anticipatory governance perspective the state is above all a state of expectations or a complex of probabilities (or chance, to use Weber’s terms, Palonen 2011; Weber 2011). These can be legitimate or illegitimate, probable or unlikely, but always in relation to certain actions. Above all, expectations of the future provide institutions and actors with power shares (Machtanteile). The power to shape the future is minimized in social-​scientific literature, but has been a well-​known trope for many philosophers. According to Montaigne, as we do not know what the future will bring, it is impossible for us to credibly argue for an alternative future to the one that powerful actors and institutions propose (Montaigne 1877, xxxi). This power to determine legitimate expectations about the future impacts directly the political order and its stability as well as the dreams (and disappointments) of ordinary citizens. A distinction central to an actor-​centred institutional approach that is not taken sufficiently into account in recent studies (cf Katzenstein and Seybert 2018; Tõnurist and Hanson 2020; Tõnurist 2021) is one between human agency and institutional agency. Human agency is more complicated and involves a practical future that is both representational and non-​ representational, involving ‘rational’ planning and emotions such as love 91

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and anxiety. These affects and attractions precede the temporal flow and orient the objects and events to which we are attracted. Often, these have to do with events that we consider dangerous, that pose an existential threat such as sudden violence or death. By contrast, institutional agency, which can involve forecasting and planning, for instance, and which is the usual bread and butter of futures research or certain institutional theories (path dependency), is representational and has to do with a symbolic future (Poli 2017, 33). The problem is that symbolic futures oversimplify not only the future but also more broadly human agency, creating an unrealistic picture of both. In the end, institutional agency is always human agency. Furthermore, given the reflexivity (which does not mean rationality in a strict sense) and self-​altering ‘nature’ of human agency as well as the performativity of anticipation, anticipation might not lead to the expected result, a collective, coordinated action that precedes and ‘fits’ future events. It might turn out to be a self-​fulfilling prophecy that is based, given the speed of changes (Toffler 1970), on an erroneous analysis of a fluid situation. It is also notable that such constellations are also prone to unintended consequences, where the incentive structures for individual actors at present might undermine the collective future of the very same activity (Baert 1991). A more useful line of action might be a corrected self-​defeating prophecy, in which an undesirable picture of the future is factually corrected as a value-​based anticipation of an ‘ideal future’. But even then, unanticipated situations that are on no one’s ‘radar’ because there are self-​evident or ‘unknown unknowns’ as well as their conflicting interpretations will emerge, impacting the definition of expected events. As part of ‘upstream engagement’ participatory diversity might enable tackling beforehand these tensions. But tools of democratic participation such as interactive platforms might also feed into a delegitimation of traditional political institutions like legislatures and elections (Fuller 2009, 209). By prioritizing discursive anticipation and its representational logic, the OECD creates an opening for alternative interpretations of future. But there might be somewhat too rosy picture of possible events and human capacities. While the OECD’s AIG does have merits of explicitly dealing with perceptions of the future and ‘ideal futures’, it also has some weaknesses. In democratic societies, governance relies on a multitude of institutionalized, legitimate anticipations and expectations or ‘simulated futures’ (Seligman et al 2016) that vary in terms of their impact on government action. However, some anticipations weigh more heavily than others. For instance, the expectations and projections produced by global ranking producers in higher education like THE or Shanghai Ranking Consultancy have gained priority compared to other imaginaries, shaping as global regulators public policies in higher education and innovation. However, some key effects of the dominance of these concerns in transnational governance have been a 92

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neglect of democracy, academic freedom and free speech (Global Public Policy Institute 2021). Indeed, in anticipatory governance of education, international organizations often appear as the guardians of its future (Robertson 2022).

Conclusions Global policy scripts define generalizable policy measures and prescribe action through predetermined sequences of events that are based on a storyline. Competitiveness has served as an underlying logic or imaginary of global governance in its various domains. This chapter has explored the global imaginaries of competitiveness in policy scripts on human capital, innovation and AI. Concerning the spatial and temporal aspects of these imaginaries, we see keen referencing to the past and invention of traditions. The historical narratives that now embody the imaginary of global competition in its different modalities describe sequences of events through process frames that fix the realm of the possible future(s), delimiting perceptions of ontology, agency and aim of policies. While such framings give a sense of orientation and help to communicate normative and causal beliefs, they are problematic because they portray a seeming continuity of activities in times of tremendous disruptions. In so doing, they rule out genuine policy alternatives and innovation that swiftly become visible, and indispensable, in times of crisis. Recently, there has been a shift towards anticipatory governance of innovation, which acknowledges the plurality in claims for the future. However, it is also necessary to recognize the political struggles and agency behind future narratives. We continue this discussion in the next chapter when we focus on the emergence and evolution of a particular imaginary associated with the global flow of ‘talent’: the mobile scholar.

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From the Medieval Scholar to the Global Flows of Academics: Exploring the Emergence, Evolution and Impact of the ‘Talent’ Imaginary Introduction In this chapter, we examine the knowledge alchemy involved in transforming academic mobility as a familiar act of academic travel to a commodified activity in today’s global competition for talent. In contemporary policy making, the assumed practices of the medieval scholar often inform the common image of an academic today. This scholar is a man (gender specific) who possesses deep and unique knowledge in his field of learning. He is perennially on the move, trekking from one centre of learning to another, sharing his latest inventions and discoveries with learned colleagues while spreading his doctrines to eager disciples. Patrons –​often royal or religious –​support his scholarly ventures: financially (by funding travel, subsistence or access to collections) and politically (by granting safe passage). A visual that emerges is one of free flow of knowledge even though the actual practices of scholarly mobility –​ especially in medieval times –​are hardly without incident (Cobban 1971, 1975; de Ridder-​Symoens 1991). So why is this image so enduring and how does it affect our contemporary debates concerning the global competition for talent? For policy makers at multiple governance levels –​university, national, regional and international –​this image is ever present because a mobile scholar generates seemingly untold benefits, not least in scientific terms, and, more recently, economic competitiveness gains and cultural diversity. We reveal that the two modalities we associate with knowledge alchemy are present in this transmutation process: first, the emergence of presuppositions 94

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concerning the connections between scientific mobility and innovation capacity, and, second, the embedding of these presuppositions into policy models (particularly in higher education policies and migration policies), as well as university recruitment practices. The interdependence of these two modalities of knowledge alchemy is so powerful that, we argue, these presuppositions persist in informing university, national, regional and international policy actors about how to be ‘competitive’ in the ‘global war for talent’; indeed, it is now a taken-​for-​g ranted approach that has been embedded in regional, transregional and city-​level strategies (see Chapter 6). As we shall also show, these presuppositions are removed from the everyday practices of academic mobility from the perspective of scholars and scientists, who are motivated by a combination of professional and very personal factors to become mobile that may have very little to do with enhancing innovation capacity. To develop our argument, we start with how scholarly mobility from the medieval era evolved to the contemporary debates about the global competition for talent. We do so by identifying the features that inform current presuppositions about academic mobility. These presuppositions are significant because they primarily highlight and emphasize the benefits of academic mobility while being less attentive to, or even overlooking, the darker aspects of this phenomenon that have become commonplace today. In so doing, these presuppositions have categorized academic mobility experiences in a binary way, with mobility being seen as positive (something to be encouraged), and immobility a negative outcome (something to be avoided, discouraged). In this chapter, we delineate the contours and discuss the impact of a narrative about academic mobility that is commonly found in contemporary institutional and policy discussions: the competition narrative. This narrative emphasizes the importance of acquiring more and more mobility experiences to shore up individual, institutional and national competitiveness vis-​à-​vis others and peers. This narrative is an extension of the broader narrative about the global competition for talent (see Chapter 4) and reflects the concerns of policy makers, institutional leaders and even individuals in their positions in comparison to others in an ever-​shifting global landscape. For institutions and countries unable to induce their faculty or citizens to engage in more academic mobility, or those that simply lack the scientific manpower, the recruitment of academic ‘talents’ (who often possess, and are assumed to be willing to continue engaging with, the desired mobility experiences) has become an accepted strategy. In this narrative, academic mobility is portrayed as a general base material for transforming individuals, institutions and nations into the most competitive. More importantly for our discussion, the effects and variations of academic mobility are conflated in the policy and popular discourse 95

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to the extent that it is generally depicted as a positive and commonplace undertaking for those seeking knowledge. While the alchemists at universities, ministries, regional organizations and international organizations are still working on a universal formula for this transmutation process, they generally agree that being the most competitive is a function of academic mobility determined by the addition and sequencing of elements such as academic destinations, duration of mobility, degree curricula and others. The precise formula is still elusive, but these elements are visible in the narratives and the policies that transform narratives into life. To describe how the competition narrative evolved and took shape, we start with scholarly mobility in medieval Europe before proceeding with academic mobility in the age of modern empires, the inter-​and post-​ war period, and today’s emphasis on the global competition for talent. We take Europe as our focal point because it plays an outsized role in informing our present understanding of the university and early practices of academic mobility (see Maassen and Olsen 2007; Yang 2016). It was also in Europe where regional policy instruments and initiatives to promote the free movement of knowledge were introduced in the 1980s and further developed (see Chapter 6). What we show is that the competition narrative is a very recent discourse based on selective interpretation or misinterpretation of the scholarly mobility experience since the medieval period. The alchemy observed is a process of transforming the valuation of academic mobility as a highly individualistic and personal experience into a universal positive accumulation of knowledge and socio-​cultural capital, even though the act of academic mobility contains far more nuances and increasingly negative effects at the individual, institutional, national and global levels. In the latter half of this chapter, we turn to the observed effects of this policy imaginary by examining the distance between the imaginary and the actual practice of academic mobility. Specifically, we are interested in whether the policies in higher education, in migration and in universities have generated the desired effects of attracting academic talents to a destination and retaining them. Looking closely at Singapore, a country located in Southeast Asia with very few natural resources and known for its highly ranked national universities and its excellent performance across other rankings (see Part I), we show that the factors that were significant in faculty mobility decisions had less to do with knowledge production and circulation and more to do with a good pay package and being close to family residing in the region. We reveal that the factors driving foreign academic talents away from institutions that actively recruit them may appear rather banal: rising costs of living and the lack of work–​life balance.

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Academic mobility in medieval Europe Academic mobility in medieval Europe began as ‘academic pilgrimages’ in which students and teachers travelled for the purpose of studying and teaching (de Ridder-​Symoens 1991, 280–​281). For students, they would start their course at the nearest university before continuing their studies at other universities farther away. What facilitated academic mobility during this period was the use of Latin as the lingua franca of instruction, which would remain until the 18th century when regional and language-​based scientific networks emerged (Jöns 2016, 314), along with common curricula and degrees (studia generalia). This seemingly ‘free flow of knowledge’ was driven by features that would be familiar to students and academics today: the desire to seek alternative ways of experiencing life (a primary motivation during the 12th century), and the geographical distance between universities known for their subject specialties (a key determinant in academic mobility during the 13th century). According to Perraton (2014, 232), ‘wandering scholars’ sought to increase their social capital, improve success in their private lives, and to have fun through academic mobility (cf ‘Order of the Wandering Scholars’ in Cobban 1971). It was not until the 14th and 15th centuries that ‘regional’ universities were created, which allowed states to ‘keep their intellectual and ideological training under observation and prevented the flight of capital abroad, detrimental to local traders and craftsmen’ (de Ridder-S​ ymoens 1991, 285). In this instance, state intervention in academic mobility was more about retaining capital than with stemming ‘brain drain’ (that is, human capital), which would only become a more dominant issue in the 20th and 21st centuries. Scholarly studies of medieval academic mobility largely revolved around students and their experiences, and were less concerned with the professoriate because of the power that students held according to how universities were organized. Two university archetypes were prominent in the Middle Ages (Aniluoto 2020). The magisterial university, exemplified by the University of Paris, was directed by a guild of masters and students as academic apprentices with no de jure rights to participate in university governance. The other archetype was the student-​controlled university, with the University of Bologna as an example. In 1193, Bologna students assumed the responsibility of appointing teachers, paying their salaries and setting out the terms of their working conditions. Teachers who arrived late to class were, for instance, fined (Cobban 1971, 40). Their approach was supported by the broader community: the Bologna commune saw the economic value and reputational prestige of a permanent university settlement, and in 1189 imposed an oath on teachers, which was designed to prevent their leaving and taking up a position elsewhere (Cobban 1971, 36). According to Cobban (1971, 44–​45),

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teachers accepted the terms because of students’ economic power and the short teaching terms –​they only had to tolerate the conditions for a limited time, and many changed their place of work as frequently as they pleased. These features driving academic mobility during this period may resonate with those working in today’s gig economy, powered by algorithms, as they seek to derive a stable income. The professoriate was able to be mobile because their licence to teach (licentia docendi) evolved into a licence to teach anywhere (ius ubique docendi) (Makdisi 1970). The first ius ubique docendi was granted in 1233 in Toulouse, but did not apply to prestigious universities such as Bologna and Paris, which accredited their own teachers (Kivinen and Poikus 2006, 190). Cobban (1975, 30) tells us that ‘It would be misleading to imagine that the ius ubique docendi provides a realistic model for the operation of the medieval university system. The ideal of a university commonwealth of teachers moving freely from one position to another among Europe’s studia generalia is one that was scarcely realized’. It is important to emphasize Cobban’s observation because the ease of communication (Latin), standardized curricula and degrees, and a licence to teach anywhere in Europe would suggest that few administrative barriers remained to prevent academic mobility during this period. So what curbed the professors’ enthusiasm to wander? According to Cobban (1975), it was the growth of salaried lectureship since the 13th century. It was financially rewarding for professors to be entrenched in these positions: ‘individuals moved from centre to centre for a definite purpose and not as part of an army in restless quest of knowledge’ (Cobban 1975, 31–​32). Medieval professors were academically mobile as part of their overall strategy to stabilize their income sources, and not because they possessed an overwhelming love for knowledge and deep desire for dissemination through travelling. Moreover, Cobban (1975, 32) highlights, ‘[t]‌he university system did not encourage a wandering scholar population per se’ for local economy and organizational stability purposes. The emergence of regional universities in the 14th and 15th centuries further contributed to the stagnation of academic mobility beyond the region (de Ridder-​Symoens 1991, 286). ‘Academic pilgrimages’ became primarily for the financial elites. Students who could not afford tuition fees and living expenses did what many students do today: they worked. A common strategy for poor students to study abroad during this period was to follow rich students and enter into their service. At the end of Middle Ages, three-​quarters of all students would stay in their regional universities. ‘The real itinerant scholar –​those who crossed Europe –​were few’, de Ridder-​ Symoens (1991, 287) tells us, ‘most of them young people keen to continue their studies in internationally renowned university and in disciplines not taught in their own schools’. Students who can afford higher education chose their destinations based on criteria that would be familiar to us today. 98

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For instance, students selected their universities based on disciplinary and university prestige (Bologna was known for medicine, canon law and civil law; Oxford and Paris for arts and theology), ease of access and geographical proximity, economic ties between towns, and university scholarships. The relationships between political entities of their academic destinations and countries of origin also played a role. For instance, most English students remained in Britain, but the Scots preferred French universities and avoided British ones; Spanish and Portuguese students went to Bologna (de Ridder-​ Symoens 1991, 298). What the literature on medieval scholarly mobility in Europe tells us is that the perceived ‘free movement of knowledge’ was far more organized, or less free or spontaneous, and driven by necessity for both students and the professors. Medieval universities in Europe also did not encourage a roving scholarly population: a stationary and stable scholarly population contributed to the local economy. For students, the general goal of academic mobility was to access specialized higher learning not available in their home regions. Once regional universities were established, students became less mobile as they stayed to study at home. For instance, Scandinavians studied abroad until Uppsala and Copenhagen universities were created in 1477 and 1479, respectively (de Ridder-​Symoens 1991, 291–​292). Similarly, Italian students remained in the region given the availability of good universities such as Bologna (de Ridder-​Symoens 1991, 298). For academics, mobility was directly related to income generation: they travelled to universities that paid them to teach. Achieving financial stability was certainly a goal that the medieval scholar would share with his 21st-​century colleagues. As we shall see in the second half of this chapter, universities and governments in Asia would embrace good remuneration packages as part of their overall strategy to attract academic talents from around the world in recent decades. These observations about medieval scholarly mobility, however, are generally absent in today’s academic mobility policy discussions. Instead, having a common lingua franca for teaching and learning, the presence of common curricula and degrees, and a universally recognized licence for teaching are strongly featured in these policy debates.

Academic mobility in the age of modern empires During the age of modern European empires, academic mobility served an organizational and educational purpose: it was a conduit that brought together European nations and their colonies in a shared imperial imaginary, as well as a way to train the colonial administrators who would oversee the day-​to-​day work of the empire. Colonial rule relied on a population educated in the way of the empire to assist with administration. Examining academic mobility to Britain, Perraton (2014) describes the role British universities 99

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played in training its empire’s civil servants and colonial elites. Indeed, as early as the end of the 17th century, West Indies plantation owners were sending their children to Britain. The students from the West Indies would go on to make up some 3.7 per cent of all new admissions in the 1760s (Perraton 2014, 21–​31). Foreign students in British universities during the 19th century would reflect the reach of the British empire. Perraton (2014, 42–​48) tells us that the students formed four groups. First, the ‘children of empire’, sent by their families to acquire a British education. Second, also dispatched by their families, the ‘children of the affluent and internationally minded middle class’ (for example, from Egypt, which had a large expatriate population then, and Russia). Third, publicly funded students who were ‘sent to acquire professional qualifications needed by their societies and not available at home’; examples of professional qualifications included ship-​ building and management, engineering, and medicine. Fourth, students from India. Students from India would go on to become ‘the largest single group, making up between a quarter and a third of the total from overseas before and between the two world wars’ in Britain (Perraton 2014, 57). Academic mobility during the age of the empires thus reflected imperial administrative needs, as well as rising middle-​class ambitions; the latter would continue to feature prominently in recent decades through the rise of the middle class from Asia and the Middle East seeking (higher) education opportunities for their children. Travelling scholarships emerged in the 19th century and proliferated in the 20th and 21st centuries as a crucial instrument for enabling academic mobility. It is important to grasp why these travelling scholarships were introduced, as the rationales behind their design and implementation remain true to this very day. The most well-​known among travelling scholarships still in existence is the Rhodes Scholarship. Created in 1901, and served as a model for the Marshall and Fulbright schemes, the Rhodes Scholarship was initially designed to bring young men from White settler colonies to Oxford with the purpose of instilling ‘into their minds the advantage to the Colonies as well as to the United Kingdom of the retention of the Unity of the Empire’ (Cecil Rhodes’ will quoted in Pietsch 2011, 723). Pre-​dating the introduction of the ‘soft power’ concept, the Rhodes Scholarship sought to cultivate an affinity with the British way of life and worldview (Pietsch and Chou 2018). For our discussion, it is interesting to point out that the design of the Rhodes Scholarship was less about encouraging the elected young men to become ‘bookworms’ (that is, seekers of knowledge) and more about bringing them together ‘to rub shoulders in Oxford’ and in so doing ‘strengthen the ties of an emerging Anglo-​Saxon world state’ (Pietsch 2011, 728). Fostering a shared worldview among the scholarship recipients, whether for the hosting country, region or for an emergent community, would be embedded in blueprints for many future travelling scholarship 100

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schemes (cf Alexander von Humboldt Foundation’s research fellowship programme in Jöns 2009). But this was not the only vision concerning the role of travelling scholarships promoted at the time. The 1851 Exhibition Scholarship set up in 1889, Pietsch (2011) tells us, offered another perspective concerning the utility of travelling scholarships during the age of modern empires. The 1851 Exhibition Scholarship was designed to benefit promising students from ‘provincial colleges of science [which included colonial institutions] to complete their studies either in those colleges or in the larger institutions of the metropolis’ (Pietsch 2011, 728–​730). While upholding the importance of ‘an expansive imperial community’, like the Rhodes Scholarship, the commissioners administering the 1851 Exhibition Scholarship ‘did not principally see their scholarships as a mechanism for fostering imperial loyalty, but rather as a means of building national scientific and industrial capacity’ (Pietsch 2011, 730). Interestingly, ‘colonial students [were] not [seen] as potential agents of imperial union but rather as what might today be called “knowledge workers” ’ (Pietsch 2011, 730). They would become ‘productive members whose skills might contribute to the development and prosperity of an expansively framed nation’ (Pietsch 2011, 730). In the competitive knowledge economy of today, the logic embedded in the 1851 Exhibition Scholarship is a familiar one: the contributions that higher education training make towards student employability. The two distinct visions embodied by the Rhodes and 1851 Exhibition Scholarships would guide future developments of travelling scholarship schemes, which often combine these perspectives and embed them within a single scheme; thus providing tension points for contemporary policy debates (cf Chou et al, 2023).

From the Great Wars to the global competition for talent The two Great Wars during the first half of the 20th century had a profound disruptive impact on academic mobility and its flow. In their analysis of the Rhodes Scholarship, Pietsch and Chou (2018) point out that it was in 1943 that the Rhodes Trust had its lowest number of awardees in history: one. The average during the 110-​year period (1902–​2012) they analysed was 67 Rhodes Scholarships per year. It was during the inter-​and post-​war periods that the competition narrative concerning academic mobility emerged more definitively, highlighting the elusive connection between mobility and competitiveness. Several developments contributed to this process. Already since the late 19th century, British universities faced the dilemma of having only a few research degrees that would meet the demands of overseas students, particularly those from the Dominions. There was simply ‘no British equivalent to the German PhD’, which was seen as the gold 101

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standard in research training to pursue (Perraton 2014, 72). This became an issue at the start of the First World War because the Foreign Office wanted to attract Canadian students to British universities, who would have gone to either the US or Germany for their studies (Perraton 2014). This resulted in Oxford introducing its DPhil degree in 1917, with Cambridge and London following in 1919 with their PhD degrees. German research universities represented the leading scientific networks in Europe up until the First World War, Jöns (2016, 318) explains, and the German model inspired many European systems to reform how their scientific research was conducted and funded. It was US universities, however, that became the most successful early adopters of the German research university model, having met its standards already in the 1890s. It was also in the US that extensive research leaves and travels were introduced as an institutional practice. Indeed, in 1880 Harvard University institutionalized research sabbaticals for their faculty and administrators, granting them paid leave after every six years of service (Eells 1962). The idea was a utilitarian one: sabbaticals enabled faculty to engage in research, self-​improvment, rest and, ultimately, increase their overall capacity to be ‘useful’ to their universities (Eells 1962; Kimball 1978). More than 50 US universities would adopt the practice by the time that Cambridge introduced research sabbaticals in 1926 (Eells 1962; Jöns 2010). ‘By enabling its academics to focus on study leave at regular intervals, the University of Cambridge encouraged overseas journey across all disciplines that increasingly targeted state-​of-​the-​art laboratories, libraries, and academic expertise in the United States’ (Jöns 2016, 321). Academic travel, Jöns (2016, 321) argues, was the ‘key research technique’ creating ever ‘closer ties between British and American Universities, and thus contributed to the emergence of an Anglo-​American hegemony in science and higher education’. While selective British universities did indeed benefit from this ever-​closer cooperation, US universities became the main winners in science and higher education following the end of the Second World War. The post-​war period saw the emergence of a unidirectional flow of academic and scientific talents from around the world, particularly from established European universities and institutions, to the US. The US government and their universities welcomed them for several reasons. First and foremost, many of those arriving in the US were scientific elites in their home countries. For instance, the 1963 Royal Society report documenting the ‘Emigration of Scientists from the United Kingdom’ during 1952–​1962 counted 20 Royal Society fellows, with ‘45 of the 224 university teachers who had emigrated holding senior American professorships or were in charge of important scientific laboratories’ (Lovell 1964). Stepping back, we know that the contributions in science the Hitler émigrés made to their countries of settlement and transit were breakthroughs (Fleming and Bailyn 1969; Coser 102

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1984; Zuckerman 1996; Pyke 2000; Medawar and Pyke 2001; Seabrook 2009; Derman 2015), many of them joined the ranks of professors and led prestigious labs in established US universities. While they found the US to be a welcoming environment, the research support and personal pay were also comparatively very generous. Highlighting the ‘American advantage’, Lovell (1964) gave one example as to why British scientists were flocking to its former colony: ‘A typical case is that of one of my young Ph.D. students who, a few years ago at the age of 26, was doing well by United Kingdom standards with a salary of $3,360 and was offered between $10,000 and $15,000 a year to work in the United States.’ Another reason why US universities were able to attract mobile academics and scientists was their institutional absorption capacity. According to a 1963 National Science Foundation report, ‘domestic institutions of higher education do not yet provide the country’s needed annual aggregate of scientists, it would seem reasonable to assume that the American scientific community could continue to absorb foreign scientists … for some time to come’ (quoted in Lovell 1964). The transformation of the US into a top academic and scientific destination was worrying for its European allies. Indeed, the exodus of scientists from the UK to North America led to a public debate in Britain about ‘brain drain’ (Balmer et al 2009), a term coined around the mid-​1960s to describe the relationship between unidirectional academic mobility and a country’s overall competitiveness –​ in science, industry, economy, and much more. The publication in 1967 of Jean-​Jacques Servan-​Schreiber’s Le Défi Américain (The American Challenge) amplified the alarm bells already ringing through the halls of universities, companies and ministries in Europe. Indeed, the warnings The American Challenge outlined would loom large in the minds of European policy makers for at least four decades as they translate the scientific distance between Europe and the US, then other rising economies, into the ‘gap discourse’ to drive supranational policy cooperation (Chou 2014; see Chapter 6). For countries losing their most skilled and educated individuals to the US, the message was clear: something must be done to stem ‘brain drain’ and reverse America’s ‘brain gain’. Competition throughout the second half of the 20th century and the first few decades of the 21st century has been fierce, with academic mobility being seen as a gauge of, and modus operandi for, competitiveness. It is during this period that we clearly observe the transformation of academic mobility as a personal, individualistic act to an assessment of the competitiveness of an academic, an institution, a system or a country (see Chapter 2 about global university rankings). Three interlinking developments have contributed to this transformation. First, the widespread recognition that knowledge is now the key mode of production for economies around the world, with the main distinctions between the third and present fourth industrial revolution being 103

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‘velocity, scope, and systems impact’ (Schwab 2016). Second, the policy responses accompanying and facilitating the transition towards knowledge economy (see Chapter 6). Here, we can identify the introduction of high-​ skilled immigration policies to attract the ‘best and brightest’ (Cerna 2016). Labour migrants are no longer a unified group with similar rights: they are differentiated between those who perform low-​skilled work, and those who are ‘talents’ and vital to a country or business’s survival (Cerna and Chou 2019; 2023). While academic mobility could simply be the movement of an educated or skilled individual from one space to another, the value seemingly generated is magical and wonderful. This sentiment is best expressed by the authors who introduced the War for Talent to the world: ‘A certain part of talent eludes description: You simply know it when you see it’ (Michaels et al 2001, xii). Third, the scope of competition has expanded: it no longer involved only those countries of the ‘West’ or ‘North’, but it now includes rapidly rising economies keen to bring back their nationals who have emigrated. What is the distance between this ‘talent’ imaginary and policy and practice? In the remainder of this chapter, we address this question by looking at the lived experiences of mobile academics and the actual conditions that motivate their mobility and immobility. We begin this discussion by situating academic mobility in the broader literature on higher education internationalization and the global academic system.

Conceptualizing academic mobility as a lived experience The higher education internationalization literature tells us that the universities and countries seeking to attract and retain mobile academics could be categorized as those at the ‘centres’ or ‘cores’ of the global higher education landscape (Schott 1998; Marginson 2008; Gerhards et al 2018). Those institutions and countries unable, or less able, to bring in and keep academic talents occupy the ‘peripheries’ or ‘margins’. Given their position at the ‘margins’, these universities and countries are, in the main, discussed in less favourable terms or not at all. This categorization of higher education institutions and countries by their ‘attractiveness’ as an academic destination has led Scott (2015, S55) to ask a fundamental question about how we perceive and understand academic mobility: ‘is the internationalisation of the academic labour force not true internationalisation at all but an aspect of global inequality and the struggle for hegemony?’. For Scott, this ‘hegemonic internationalisation’ perspective is limiting, and he argues for embracing the dynamics of academic mobility as one of ‘fluid globalisation’ in which ‘flows of students and staff [should] now be seen in the wider context of fluid and contested forms of globalisation that can no longer be regarded as 104

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simply an expression of the existing global order’ (Scott 2015, S55–​56). For our discussion, this means that it is essential to move beyond the academic mobility–​immobility binary where mobility is simply depicted as a positive and desirable act, and immobility as a negative act to be avoided by individual scholars, universities or even countries. Indeed, it is also essential to explore how academic mobility could be a negative experience for scholars, and immobility a positive one that scholars embrace. Examining the literature on transnational mobility, Bauder (2015, 83) tells us that the conventional wisdom highlights how ‘migration exposes academics to new contexts and unleashes creative forces that propel scientific knowledge production’. Academic destinations profit from their ability to attract and retain highly mobile academics, who are seen as knowledge workers and keystones in a university or a country’s transformation from laggard to forerunner in the global economic competition. In this perspective, academic mobility is portrayed in a positive light. Those academics who stay are thus seen as being stuck in an environment that has now been made less attractive by the departure of their talented colleagues. By extension, the sending universities and countries are also the ‘losers’ because they failed to retain the talented individuals, a situation that is seen as even more costly in scenarios in which public monies have been used to educate and train those academics who left. In these instances, academic immobility and the overall failure to retain mobile scholars are portrayed in very negative light. In studies of contemporary higher education, mobility as a negative experience has increasingly become a prominent topic and reflects dominant features of the global higher education landscape (Fahey and Kenway 2010; Robertson 2010). Existing literature has pointed to the prevalence of employment precarity in the university sector, highlighting the following causes: continual cuts in university operational budgets affecting the numbers of available permanent positions (Rubin 1977; Huisman et al 2002); overproduction of PhDs worldwide (Geiger 1997); and greater competition for ever fewer positions. This competition for academic jobs is now global as administrative barriers to entry –​at the national level, and at the regional level (Chou and Gornitzka 2014) –​are removed to welcome eager knowledge workers from around the world; indeed, the engagement of city-​level administration in the global talent competition highlights the ubiquity and intensity of this competition (see Chapter 6). Adding to this is the growth of strategic international recruitment of faculty members as part of internationalizing the university (Horta 2009), reflecting the recognized significance of global university rankings (see Chapter 2). These developments impact and steer academic mobility: scholars are forced to become mobile (either geographically or sectorally) to secure better working conditions and ensure job security. The literature has highlighted ways in which universities and countries seeking to become academic 105

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destinations could capitalize on the increasing casualization of knowledge work, the erosion of tenure systems, and the growth of ‘low-​paid highly-​ skilled’ and ‘permanent postdocs’ (Bauder 2015; Courtois and O’Keefe 2015; Morgan and Wood 2017; Manzi et al 2019) by providing comparatively more favourable conditions, such as job stability. For these universities and countries, appealing to academics’ professional and personal preferences may very well transform their institutions and systems into academic destinations. For those universities and countries that have already achieved the status of ‘destination’ in the global academic system, mobility as a negative experience reveals the ways in which scholars may be ‘slipping’ away from the ‘cores’ to other parts of the global higher education landscape. In studies of academic mobility, less emphasis has been placed on immobility as a positive experience. Indeed, the overwhelming focus on academic mobility and ensuring the movement of scholars, the very idea that immobility could be a positive experience for the scholars and scientists, as well as the institutions that retain them, has garnered far less attention. What the literature has identified is the significance of professional or career attainment and family circumstances as strong anchoring mechanisms resulting in immobility. Through knowledge alchemy, the valuation of academic mobility as a universally positive and desirable outcome has been translated into strategy and policy across multiple governance levels –​macro-​ regional, transregional, national, and even at the city level (see Chapter 6), but not necessarily in practice. It is important to acknowledge that not all higher education systems reward mobility. For instance, in higher education systems such as those in China, Japan and Italy, where academic progression and promotion partly relies on seniority and academic social relations, mobility means exiting the system and severing ties, or maintaining them at great costs. By staying within the system, the faculty demonstrates loyalty and continuous contribution (that is, observable). Immobility may thus be far more desirable than academic mobility, which adds risk. The propensity to be mobile thus decreases with professional attainment (Ackers 2005; Nerdrum and Sarpebakken 2006; Bauder 2015) and for those established in higher education systems that see less value in academic mobility, longer-​ term mobility such as taking up a new position is likely to mean starting over in a new academic system. ‘The tendency for mobility to become more “sticky” over the life course’, Ackers (2005, 114) tells us, ‘might thus restrict subsequent mobility (and the propensity to return) for those scientists who establish partnerships and families’. Here, immobility (after initial mobility) may be the result of family circumstances. In his research, Bauder (2015, 91) argues for the importance of family formation in studies of academic mobility: ‘Even as mobility improves the positions of academic workers, personal relationships may suffer, and accompanying spouses and family members may experience 106

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distress.’ In these instances, divided perspectives about the ‘added value’ of mobility may lead to immobility, less mobility or transnational families. By choosing to be immobile, we may see these academics as choosing to become ‘embedded’ in their universities and research traditions. For our discussion, immobility as a positive experience actually points to the overall capacity of higher education institutions and systems in retaining academics. From an experiential perspective, the existing literature suggests that there are four categories of academic mobility and immobility. First, reflecting the projected imaginary of mobile talents, academic mobility can be a positive and transformative experience for scholars and their chosen institutions and countries. Mobile academics offer their knowledge and skills to the receiving higher education institutions and systems and thus contribute to their position in the global academic system. When these mobile academics stay, they then become embedded in the receiving system (immobility as a positive experience). The literature also reminds us that there is a dark side to academic mobility and immobility. Academic mobility as a negative experience mainly points to the exploitative features of contemporary higher education where scholars are forced to become mobile to secure new positions in another institution, country or industry. This feature has been discussed as precarity in academic employment –​ an increasingly common feature of today’s university sector. Academic immobility as a negative experience is observed when scholars are unable to move, and are thus ‘stuck’ in their universities and countries. Together, these four categories allow us to discuss the distinct features of academic mobility as one phenomenon and the implications for countries and higher education institutions seeking to become attractive academic destinations. In the rest of this chapter, we will examine the case of Singapore to identify the factors that had contributed to and undermined its status as an academic destination.

Attracting and retaining foreign academic talents in practice: Singapore Using the case of Singapore, we discuss the lived experiences of foreign faculty, particularly the factors that motivated them to relocate and remain in the city-​state. Our aim is to confront the existing ‘talent’ imaginary with the real practice of academic mobility in an academic destination on the rise, which we discuss in what follows. We chose Singapore as our case study because it is a revealing case. It is a country where English is commonly used and is an official language alongside Mandarin, Malay and Tamil. In addition, Singapore has a very stable political system: the People’s Action Party has ruled the country since its founding. With a reputation of ‘punching above its weight’ in multiple fields and sectors (see Part I), the Singapore government 107

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has been consistently explicit with the aim of its talent migration policies: the city-​state wants to attract the ‘best and brightest’ from around the world to contribute to its economic growth (Cerna and Chou 2014; 2023a; 2023b; Yeoh and Lam 2016). As a country with very little natural resources beyond its population, the Singapore government has welcomed foreign talents to the city-​state since its independence in 1965. To what extent do the factors motivating foreign academics to select Singapore as a destination support the ‘talent’ imaginary that informs policy design? With a population of 5.64 million as of June 2022, Singapore has a population breakdown consisting of 61–​64 per cent Singaporeans, 9–​10 per cent permanent residents and 27–​30 per cent foreigners (see Table 5.1). The percentage of foreigners in Singapore is high, even though the total numbers have been fluctuating (see Figure 5.1). In the US, which has the most immigrants in the world (44.8 million in 2018), the proportion of immigrants in the country is about 13.7 per cent of the total population (Budiman 2020). What also makes Singapore an interesting case for examining the relationship between the ‘talent’ imaginary and policy effects is how the government’s overall liberal immigration policy for attracting foreign talent has generated tensions in the recent decade (Cerna and Chou 2023a; 2023b). In a country where public demonstrations require first securing licences from the government, Singaporeans have increasingly voiced their concerns that the current accommodation of foreigners is unsustainable, pointing to the infrastructural crush (in housing, public transportation, education, and more) as one of the many reasons that the government needs to rethink its immigration and population policy. For the Singapore government and the businesses and institutions that actively recruit foreign talents, the implementation of the ‘talent’ imaginary into policy and practice must be set against this context. Singapore’s university sector has benefited from the recruitment of foreign academics, who have contributed to the increased visibility and recognition of its universities in the global higher education landscape. For instance, the flagship universities of Singapore –​the National University of Singapore (NUS) and Nanyang Technological University (NTU) –​have ranked high in a variety of international university rankings in the recent decade (see Table 5.2). NTU, a university better known for its engineering faculty but that now includes arts, humanities and social sciences faculties, has had a phenomenal rise across known global university rankings. The increasing prominence of Singaporean universities in the global higher education landscape, the use of English in a multilingual environment, and a highly stable political system have all contributed to making the city-​state a generally attractive destination for mobile academics –​and thus a suitable case to identify the factors that matter in real academic mobility decisions. 108

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Table 5.1: Singapore’s population breakdown (in thousands and percentages) 2013

2014

2015

2016

2017

2018

2019

2020

2021

Total

5,312.4

5,399.2

5,469.7

5,535

5,607.3

5,612.3

5,638.7

5,703.6

5,685.8

5,453.6

Citizens

3,285.1

3,313.5

3,343

3,375

3,408.9

3,439.2

3,471.9

3,500.9

3,523.2

3,498.2

PRs

533.1

531.2

527.7

527.7

524.6

526.6

522.3

525.3

521

488.7

Non-​Res.

1,494.2

1,554.4

1,599

1,632.3

1,673.7

1,646.5

1,644.4

1,677.4

1,641.6

1,466.7

Work Permit

46%

46%

46%

45%

44%

42%

41%

41%

41%

39%a

Dependents

15%

15%

15%

16%

16%

17%

17%

17%

17%

18%

FDWs

13%

13%

13%

13%

14%

14%

15%

15%

15%

16%

EPs

12%

11%

11%

11%

11%

12%

11%

11%

12%

11%

S Pass

9%

10%

10%

11%

11%

11%

12%

12%

12%

11%

Students

6%

5%

5%

4%

4%

4%

4%

4%

4%

4%

a

Notes: PRs =​permanent residents; Non-​Res. =​non-​residents; EP =​Employment Pass; FDW =​foreign domestic workers; dependents category includes dependents of citizens, PRs and Work Pass holders; a the 2020 and 2021 reports provide two percentages for Work Permit holders, divided into those working in ‘Construction, Marine Shipyard and Process (CMP)’ (20 per cent for 2020 and 2021), and those in non-​CMP sectors such as ‘Services, Manufacturing’ (21 per cent for 2020, and 19 per cent for 2021). Source: Authors’ compilation from Population in Brief (2012–​2021), Prime Minister’s Office, Singapore.

From the Medieval Scholar to Global Academics

109

2012

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Figure 5.1: Singapore’s non-​resident population (2012–​2022) Work Permit

Dependents

FDWs

EPs

S Pass

Students

Total

1,800,000 1,600,000 1,400,000

Number of people

110

Knowledge Alchemy

1,200,000 1,000,000 800,000 600,000 400,000 200,000 0

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

Year Source: Authors’ calculation based on percentages given in Population in Brief (2012–​2021), Prime Minister’s Office, Singapore.

2022

newgenrtpdf

NUS

THE World

2012

34

40

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

29

26

25

26

24

22

23

25

25

21

19

THE Asia

–​

–​

2

2

2

1

1

1

2

3

3

3

–​

QS World

28

25

24

24

22

12

12

15

11

11

11

11

11

3

2

2

1

1

1

2

2

1

1

1

1

–​

174

169

86

76

61

55

54

52

51

48

47

46

111

QS Asia NTU

2011

THE World

36

THE Asia

–​

–​

11

11

10

2

4

5

6

6

5

5

–​

QS World

58

47

41

41

39

13

13

11

12

11

13

12

19

QS Asia

17

17

10

7

4

3

1

1

3

2

3

3

–​

Source: Authors’ compilation from Quacquarelli Symonds (2011; 2012; 2021a; 2021b; 2021c; 2022a; 2022b), The Guardian (2011, 2013), The Indian Express (2017), and Times Higher Education (2021; 2022a; 2022b).

From the Medieval Scholar to Global Academics

Table 5.2: Singapore’s universities in THE and QS university rankings (2011–​2023)

Knowledge Alchemy

Our analysis draws from a rich dataset collected for a mixed-​methods study on Singapore and the global competition for talent in the university sector.1 Specifically, we use data from a large-​scale survey of, and semi-​ structured interviews with, tenured and tenure-​track faculty at NUS, NTU and Singapore Management University to identify the factors motivating academics to come to and remain in Singapore. These three universities in Singapore were selected because they have the highest undergraduate enrolment at the time. The survey invited 2,691 tenured and tenure-​track faculty members to respond in November 2015, and a total of 616 faculty members completed the survey, and 91 partially completed (total response rate =​26 per cent). Ninety-​eight in-​depth semi-​structured interviews were carried out between June 2015 and June 2016 following the ‘snowball sampling’ method with faculty members from four major fields: Science, Technology, Engineering and Mathematics (STEM), Social Sciences, Humanities, and Professional Schools. The data delineated two distinct narratives concerning academic mobility, as well as immobility, in the case of Singapore. To start, both narratives first support the ‘talent’ imaginary by describing mobility to Singapore as a positive experience, revealing the respondents’ perception of Singapore as an attractive academic destination. The narratives then diverged when the respondents were asked about their future mobility plans, particularly their intentions to remain in Singapore or move to another academic destination. These divergent narratives are telling because they point to differentiated effects of the same policy implemented. The unevenness in policy effects is often not taken into consideration when deliberating policies steered by the ‘talent’ imaginary.

The making of Singapore as an attractive academic destination Respondents identified three sets of factors as significant in their mobility decision to relocate to Singapore: a highly competitive remuneration package and easier access to research funding; the use of English at work and in everyday life; and moving closer to parents for respondents from Asia. In the case of Singapore, the following components are generally included in a typical remuneration package for those taking up a position in one of its autonomous universities: a relocation package that covers moving expenses to and from the city-​state, flights, and other transportation costs; a

1

The study on Singapore was supported by the National Research Foundation, Singapore, under Grant SRIE 023. Any opinions, findings, conclusions and/​or recommendations expressed in this chapter are those of the authors and do not necessarily reflect the views of the Singapore National Research Foundation. 112

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competitive monthly salary, potentially including performance bonuses; tax incentives for foreigners; and time-​specific housing and educational subsidies for non-​locals. About 70 per cent of the survey respondents said that the remuneration package was ‘important’ or ‘very important’. A Thai associate professor explained the difference between Australia and Singapore as follows: ‘First, the tax, the income tax [in Australia] is very high, about 50 per cent, 48 something, 48.5. And you hit that grade very fast. … So you take in pocket only 60 or 50 something. That’s one thing. … But another factor is most faculty members don’t send their children to local school! They send to the private school. … Third, I check around the day care. Wow, the day care is extremely expensive! The price per day is –​in the US –​the price per month! … Fourth, housing expensive! It’s not cheap at all! … Dollar wise, over here [in Singapore] still slightly more [laughs].’ (A10) A Taiwanese associate professor saw taking up the position in Singapore as a financially responsible decision for her family: ‘Okay, at the time, we did think about [relocating to Taiwan]. My husband went to Taiwan and [did a] four-​month exchange programme. … But at the end … the problem is because my parents-​in-​law. I still have to support them. So our salary over there will not be able to cover anything like medical bills. Nothing. We cannot do anything.’ (C13) Easier access to research funding was also highlighted by the survey respondents as instrumental in their mobility decision. For 80 per cent of those who received their PhDs during 2013–​2015 and 65 per cent who earned their doctorates before 2000, access to research support was cited as ‘important’ or ‘very important’. A Chinese assistant professor working in the STEM fields described the difference between the US and Singapore as follows: ‘[I]‌n the US, it’s like if you don’t get funding you, you don’t have students right. So anyway they will be actively looking for funding whereas here, the school is very supportive of PhD students and also [funding] support for like visiting students. … I think in the US, from what I heard from my friends, they will need to spend all their time writing proposals to get funding.’ (B9) A French associate professor shared the same sentiment: ‘[W]‌hen I talk to my friend[s] who are in France and who do a similar job, I know that I am privileged. So basically [the] size of my lab is 113

Knowledge Alchemy

much bigger than what I could expect to have in France or probably in the US as well. The reason why my group is big is because my PhD students are … all on scholarships and I don’t pay their scholarships. The system really allows you to have a lot of students as long as you can manage them.’ (C14) Singapore has a policy of ‘English-​knowing bilingualism’ (Pakir 1991; Ng 2016; Goh 2017), which sees English as being commonly used at work and in everyday interactions. Respondents born in non-​English-​speaking countries (for example, France, Italy, Japan, Germany), as well as those from English-​speaking countries (UK, US, Canada, Australia), all indicated that ‘English is used in work environment’ as ‘important’ in their mobility decision. An American professor succinctly framed the decision to come to Singapore instead of taking up a position in French-​speaking Canada as motivated by linguistic concerns: “Well I don’t speak French” (B1). Learning a new language for work is seen as a strong barrier. For 56 per cent of the respondents, the factor ‘able to communicate in English’ outside of work was ‘important’ or ‘very important’. A Thai associate professor explained that he wanted his children to be educated in English, which was impossible in Thailand on an academic salary from Thai universities (A10). For some faculty, the multilingual environment in Singapore was very attractive. A Chinese associate professor highlighted the combination of English and Chinese as ideal for his family (A14), while a Chinese assistant professor pointed to the familiar linguistic environment between Singapore and China: “Because as a Chinese, I feel –​I don’t feel the cultural difference between Singapore and my hometown” (A8). A Filipino assistant professor offered a similar perspective: ‘Well, I guess it also helps that I’m ethnic Chinese so I can speak, yeah this is one of the things many people felt that was difficult because most of the people here speak Mandarin –​but I learned Mandarin in school when I was in Manila, but I don’t speak Mandarin with my parents or my grandparents. I speak dialect with them [Hokkien] but there are some Hokkien people here as well, so I can speak with them using Hokkien.’ (A11) ‘Moving closer to parents’ was significant for those faculty born in Asia; more than 80 per cent of these respondents obtained their degrees outside of the region, and more than 60 per cent studied in the US and the UK. An Indian assistant professor who did his PhD in the US saw Singapore’s proximity to India as one of the reasons his family moved to the city-​state: “my wife’s mother had a heart attack while we were in the US and it was very difficult to manage from a distance” (A9). Turning down offers from the US and 114

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Europe, a Malaysian associate professor expressed similar thoughts of wanting to be closer to his ageing parents: “At that point they were like old and ail[ing], and frail. … I like that Singapore is closer to Malaysia” (A5). These faculty saw their Singapore position as an attractive compromise between a more prestigious position in the ‘West’ far away from their families, or a less prestigious position with fewer resources in their country of origin where their families lived (Ortiga et al 2018; 2019). While the conditions of Singapore and its universities were able to attract foreign academic talents to join its universities, distinct features about Singapore and its academic institutions are also driving foreign faculty away –​as we examine next.

Moving away from Singapore: increased living expenses, no work–​life balance The rising cost of living in Singapore and the lack of work–​life balance were the two significant factors motivating foreign faculty to consider moving away from this emerging academic destination that initially attracted them. Thirty-​nine per cent of respondents who intended to stay in Singapore reported that they were either ‘dissatisfied’ or ‘very dissatisfied’ with the cost of living; this percentage increased among those who intended to leave –​60 per cent, but was the highest among those undecided about whether they will remain in the city-​state –​66 per cent. An Indian assistant professor shared that: ‘Singapore is starting to be quite expensive for us because the first kid is going to international school. Then, [my university] stopped giving subsidies. … I was not keen on sending them to local schools. And after nine years we have to move out of faculty housing and we have to find money to buy our own place so it’s getting to be expensive. I found that the insurance coverage, like my pregnancy, I had to pay from my pocket.’ (C6) When housing and educational subsidies that Singapore universities offered to their foreign faculty expire, the rising living expenses make the city-​state extremely unattractive. This is because the loss of subsidies is not compensated by a comparable increase in wages, thus foreign academics who have secured promotion and are tenured may be earning less than when they first started at a university in Singapore. In Singapore, 80 per cent of the residents live in affordable government housing, with the purchasing of such a unit accessible only to citizens and permanent residents. During the COVID-​19 pandemic period, like many countries around the world, Singapore monitored closely and restricted the overall number of people who were allowed to enter and leave its territory. As a result, the foreign construction workers who would 115

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ordinarily be admitted were not allowed entry. This had a direct effect on the number of housing units available in Singapore, with the limited availability pushing up rental and purchasing prices. In response to increasing housing prices, the Singapore government in December 2021 increased the buyer’s stamp duty foreigners must pay when buying their first property in Singapore: from 20 per cent to 30 per cent; no changes were introduced for Singaporeans (0 per cent) and permanent residents (5 per cent). A European associate professor, who adopted a child while living in Singapore, explained that she took on the position of resident advisor at one of the campus dormitories to avoid paying high rents outside the university when her campus housing lease could not be extended. For her, this was like having “one and a half job[s]‌”, and the resident advisor position consumed her research time (C11). Another European associate professor described the rising living expenses in his case. He sent his two children to an international school in Singapore, a common approach non-​Singaporeans have adopted either as a preference or because they were not able to enrol their children in local schools. For this respondent, the cost of his children’s education doubled in six years when his university withdrew the subsidies. According to him, “the price of international schools [in another Asian country where he was previously] remain stable. Not like here where it has really gone up. When I came … tuition [there] was as much as Singapore, now it is only half of what we have to pay here” (A12). These responses indicate that Singaporean universities did not adjust the basic compensation for foreign faculty when the cost of living in the city-​state effectively reduced their pay package. A survey respondent expressed the following concerning living expenses in Singapore: ‘The biggest barrier/​reason to leave Singapore, BY FAR, is the cost of education which requires that, as foreigner, I have limited access to local schools and must pay over $65,000 [€42,500] a year to educate my children at international school. I believe this should be subsidized. I also believe that academics (particularly senior academics) should be paid enough to have a choice to live off campus, rather than in giant box style apartments on campus. At the moment, the cost of education and (in my case) car and rent (things that are considered basic in any Western country but a luxury here) means that Singapore is not a sustainable long-​term option as a tenured academic.’ (SR1) Turning to work–​life balance, many survey respondents reported their dissatisfaction. Among those who had worked in Singapore for less than nine years, 36 per cent said that they were ‘dissatisfied’ or ‘very dissatisfied’ with existing work–​life balance. For those who have not decided whether they would remain or leave Singapore, 49 per cent reported that they were 116

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‘dissatisfied’ or ‘very dissatisfied’ with the demands of both work and life. For those who intended to leave Singapore, dissatisfaction was, unsurprisingly, the highest: 56 per cent indicated that they were ‘dissatisfied’ or ‘very dissatisfied’ with their work–​life balance. Explaining the challenges in more detail, a survey respondent stated that working and living in Singapore has been an “[o]‌verall a safe and good experience. But the lifestyle (work) is too hectic and it is hard to draw a line between work and home!”. “I feel like I am living here for a job –​a good job indeed”, another respondent explained, “but I do not feel that I am living a life here”. For a respondent with elderly parents outside of Singapore, “It is hard for me to balance my work and my responsibility to take care of my aging parents”. Another respondent added, “[The] 24/​7 365 work culture makes it difficult to get away and think”. Increased living expenses may drive academics towards affordable destinations, but the lack of work–​life balance suggests mobility of another kind: moving away from the academic field, university or, perhaps, knowledge work. This outcome would indeed be very far from the scenario envisioned by the ‘talent’ imaginary –​as we discuss further in the next section.

What does the case of Singapore tell us about the ‘global’ flows of academics? What do these findings reveal about academic mobility and immobility in contemporary academe? And what do they tell us about the relationship between the ‘talent’ imaginary and policy effects in an attractive academic destination? The case of Singapore confirms that the elements contributing to the making of an attractive academic destination are as follows: competitive remuneration package; easier access to research funding for faculty; the use of English at work and in everyday life; and, for those originating from the region, moving closer to parents. For the surveyed and interviewed faculty, mobility to Singapore was a very positive experience. Professionally, many pointed to an improvement in their statuses such as having higher incomes or well-​staffed labs. Personally, they indicated high personal satisfaction with being able to better provide for their families, whether it was taking care of ageing parents or giving their children an English education in a multi-​ethnic society. The sentiments expressed generally support the ‘talent’ imaginary, as foreign faculty have contributed to the growing recognition of Singapore’s universities in the global higher education landscape. Singapore is certainly not unique in having universities with institutional policies and practices that speak to these individual-​level preferences. For instance, a generous pay package, great research support, and use of English at work are now common features in countries where English is not an official language and having a globally competitive academic system a significant national priority. Programmes targeting academic diaspora are also common 117

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in Asia. Indeed, as a region that has long seen its nationals immigrate to the ‘West’ to study and remaining to work, governments in Asia have developed programmes to bring back their ‘best and brightest’ as part of their overall revamping of national knowledge systems. The standardization of ‘how to attract the best and brightest’ suggests the prevalence of the ‘talent’ imaginary even in the ‘non-​centre’ of the global higher education landscape (see Chapter 6). The ability of Singapore’s government to work together with its autonomous universities to administer a policy encompassing and highlighting these factors to potential academic hires has been instrumental in the transformative ascent of its flagship universities in the global rankings. Continuous transformation, however, relies on sustained changes. What this means in practice is that a higher education system must be able to embed those already successfully recruited while continuing to attract talented faculty. Put differently, for an academic destination to retain foreign faculty who contribute to university performances, both mobility to the country and subsequent immobility must be positive lived experiences. In the main, sampled faculty from Singapore were happy to be immobile for the foreseeable future, but they also pointed to how two factors over time diminished their professional and personal statuses: the rising cost of living and lack of work–​life balance. As housing and educational subsidies expired, faculty members questioned whether the once attractive remuneration packages were sufficient compensation for remaining in Singapore as they experience real loss in wages in comparison to their starting salaries. Negotiating an academic exit was tricky for faculty with working spouses or school-​aged children. Their immobility in the city-​state became a negative experience as they adjusted or struggled with the now additional living expenses, which became prohibitive for some when their universities ceased subsidies and did not adjust compensation in line with the rising costs of living. Difficulties with achieving a work–​life balance is not unique to faculty based in universities in Singapore. For surveyed faculty members, as their lives in Singapore became increasingly defined by work (‘living here for a job’), they aspired to become mobile again. In these instances, their mobility out of Singapore is encouraged by their cumulative negative experiences in the city-​state. Even as policy makers and university leaders worked towards consolidating Singapore’s position as an attractive academic destination, growing dissatisfaction concerning work–​life balance among recruited foreign faculty paints a different picture of the once attractive destination. Indeed, it points to the ‘talent’ imaginary pushing policy makers and institutional leaders to focus on attracting the ‘best and brightest’, but failing to retain recruited talents through subsequent measures that would address their changing needs. Work–​life balance has also emerged as a major theme during the COVID-​19 pandemic. Many people are leaving their jobs to explore other options. The opposing policy dynamics of attracting 118

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and pushing away may ultimately neutralize, or reveal a dominant force. In the meantime, the distance between the ‘talent’ imaginary and observed effects of academic mobility is wide and points to the knowledge alchemy prevalent in this sector.

Conclusions Studies of academic mobility have long fascinated policy makers and scholars alike. This is hardly surprising given that the act of academic mobility embodies many features of interest. For instance, academic mobility raises many social, economic and political issues of contemporary concerns, ranging from access to quality, equity and excellence. The practice of academic mobility also involves many actors inside and outside of the academe: not only junior, mid-​career, senior and ‘star’ professors, but also funders, research administrators, and even migration officials and union members. Academic mobility is thus an affair involving multiple governance levels, from individual, household, departmental and institutional to national, regional and even international. These basic features alone make academic mobility a captivating topic for many, but it is the elusive promise of being the ‘most competitive’ (the ‘gold’) that academic mobility offers which continues to lure institutional, national and supranational leaders, as well as scholars and scientists themselves, to encourage, continue and expand the practice. In this chapter, we described and identified the narrative about academic mobility that is prevalent in today’s institutional and policy discussions: the competition narrative. Imaginaries drive strategies and policies, but to understand the limitation of knowledge alchemy in effecting the desired outcome, it is also essential to look at what happens in practice. In this chapter, we explored the lived experiences of foreign academics in the case of Singapore to identify the professional and personal factors that contributed to their mobility decision to come to the city-​state, as well as the factors that may lead to their departure. While the combination of excellent pay packages, easier access to research support, and the use of English at work and in everyday life may appear magical to the foreign faculties who arrived in Singapore, this ‘magic’ wears off over time. As foreign faculties experience the working environment of universities and the rapidly rising costs of living in the city-​state, they question whether Singapore is an academic destination for them. It is this questioning that marks an opening for agency, apparent in the mundane everyday life and choices of ‘global talents’ in Singapore. In the next chapter, we turn to the prevalence of the ‘talent’ imaginary and its accompanying competition narrative around the world. Their ubiquity in strategies and policies at the institutional, city and beyond-​the-​state levels point to the widespread practice of knowledge alchemy in contemporary governance. 119

6

Strategies and Policies for the Global Talent Competition Introduction In this chapter, we describe how policy makers and decision-​makers developed and implemented strategies and policies based on the ‘talent’ imaginary and bring knowledge alchemy to life. By reviewing how the presuppositions revolving around the global competition for talent became integrated in higher education and university policies, migration policies, university recruitment practices and more, we show how the processes of globalization, internationalization and competition unfold across multiple policy sectors, institutions and governance levels. Here, policy makers and decision-​makers use the notion of interdependence in multiple ways: to describe the state of existence, to identify the sources of cause for concern (interdependence as a policy problem), and to present policy solutions (interdependence as an opportunity for new alliances and cooperation). To unpack how interdependence is interpreted and used, we organized this chapter to revolve around several case studies that span multiple governance levels: the macro-​regional, the national and transregional, and at the city level. Our case selections are meant to be illustrative and not comprehensive, involving cities, countries and regions in the ‘West’ as well as the ‘East’. What our cases have in common is the centrality of the ‘talent’ competition imaginary, articulated through the competition narrative, driving knowledge governance. We start with developments taking place at the beyond-​the-​state level, looking particularly at how the EU integrated the presuppositions about global talent competition into its strategies and policies concerning creating a ‘knowledge area’, big technology and AI. We then examine China and its university alliance-​building efforts through the Belt and Road Initiative. Our discussion shows how China is strategically positioning its universities at the centre of the global web of connectivity through differentiated approaches 120

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of university alliance building. Finally, we turn to city-​level developments and unpack how cities are becoming global players in talent competition. What the various developments taking place across multiple governance levels reveal is how prevalent the practice of integrating the imaginaries of competing for ‘talent’ has become. Indeed, knowledge alchemy has reached a fevered pitch and its allure is no longer confined only to national actors working in one policy domain: it is now widespread and highly contagious to anyone everywhere susceptible to yearnings of being ‘competitive’.

The Europe of Knowledge The EU has been a frontrunner among regional organizations in integrating the ‘talent’ competition imaginary in its policy work. In 1997, the European Commission proposed a series of actions to reform internal policies in preparation for the Eastern Enlargement. The proposal was organized under the heading ‘Towards a Europe of Knowledge’, and the European Commission argued for the role that ‘[c]‌ommunity action in the areas of education, training and youth’ would play in making policies that are ‘knowledge-​based’ and raising the ‘level of knowledge and skills of all Europe’s citizens in order to promote employment’ (European Commission 1997). Since then, the ‘Europe of Knowledge’ has been used in different ways to refer to the role of the university in the transition towards what is referred to as knowledge economy and society, the role of knowledge in contributing to Europe’s job and growth agenda, and the linkage between science, research and smart policy making. For scholars (Elken et al 2011; Chou and Gornitzka 2014; Chou 2016a), the Europe of Knowledge has been primarily used to refer to activities encompassing the making of the European Higher Education Area and the European Research Area. As we show here, debates concerning AI emerged more recently in broader discussions concerning the Europe of Knowledge, and reflect the overall sectoral demarcation that have organized EU-​level policy work. Discussing the respective ‘making’ of European Higher Education and Research Areas will demonstrate how the presuppositions concerning academic mobility has been embedded into activities at the supranational level. The making of European Higher Education Area dates to post-​war Europe, when the role of universities in the reunification and reconstruction of Europe was considered (Corbett 2003; 2005; 2009; 2012). The result was the classic intergovernmental compromise: a reference to constructing ‘An institution of university status’ in Article 9.2 of the 1957 Treaty establishing the European Atomic Energy Community and the acceptance that the European Community, as the EU was then known, would oversee vocational training, which European policy makers saw as significant in achieving the economic objectives of European integration. Following the 1985 Gravier 121

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ruling (European Court of Justice 1985), which carved out the European Community’s role in higher education, European education ministers introduced an action scheme for university student mobility in 1987. The Erasmus programme would become synonymous with Europe’s success in enabling the free movement of students and other university personnel (Chou and Ravinet 2017). By supporting the mobility of students and university staff, the Erasmus programme sought to simultaneously promote the creation of a shared European identity and skills acquisition (see travelling scholarship scheme discussion in Chapter 5). The shift towards giving employability issues greater emphasis in European-​level policy work would come to shape the Erasmus programme in the decades after. Meeting in May 1998, the higher education ministers of France, Germany, Italy and the UK called attention to the desirability of comparable degrees and a two-​cycle system for improving student mobility and employability. The resulting Sorbonne Declaration led to the launch of the Bologna Process in June 1999, which committed the member countries to coordinating their national policies along six common objectives. As a non-​ EU process, the Bologna Process enabled the signatories to steer existing and new initiatives towards shared aims in a policy space previously filled by a collection of initiatives promoting exchange and mobility between different national systems with different degree structures (Ravinet 2008). Through the Bologna Process, mobility experiences could now contribute to the broader accumulation of degree credits across very distinct university systems where a study unit is measured differently. The perceived success of the Bologna Process in triggering reforms across Europe (and beyond) has transformed European efforts into a powerful ‘brand’ for institutional, national and regional policy actors keen to modernize their higher education (Scott 2012; Chou and Ravinet 2015a; 2015b; Vukasovic 2016). Indeed, cooperation between universities, with or without state steering, is now commonplace and manifests deep internationalization in some instances. In comparison to the making of the European Higher Education Area, the construction of the European Research Area has largely taken place within the supranational framework. Research policy cooperation was seen as having a direct contribution to Europe’s economic recovery and industrial development (Guzzetti 1995), and this occurred in the areas of agriculture, energy, health and environment. It was in the 1980s, when the European Commission launched the multi-​annual research funding schemes, that we saw European research policy cooperation taking on a definitive shape. Initially referred to as Framework Programmes, then Horizon 2020 and Horizon Europe, these multi-​annual research funding schemes characterized European research policy cooperation as distributive (Banchoff 2002). This defining feature would change with the launch of the European Research 122

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Area in January 2000. The European Commission situated the European Research Area as part of its broader reform efforts under the Lisbon Strategy to improve the competitiveness of Europe vis-​à-​vis its rivals (then Japan and the US, now also China and the emerging countries) (Chou 2014). As the Lisbon Strategy amplified the need for the EU to compete globally, the ‘talent’ competition imaginary became embedded in the making of the European Research Area. The European Commission’s argument goes that the ‘European system’ is a composite of different research systems, with the formula of number of member states plus the EU as representative of this state of fragmentation. The solution was that all EU member states and stakeholders should work together towards an open scientific space within which scholars and scientists would circulate freely to collaborate, share ideas and, eventually, innovate and bring the ideas to market. The ambition of the European Research Area was such that the Fifth Freedom –​the free movement of knowledge –​was coined and added to the Four Freedoms (free movement of goods, capital, services and people), which have served as the cornerstones of European integration since the 1950s. Indeed, the emphasis on knowledge was so strong that it was revealed that the European Research Area was to replicate the Single Market, the foundational project of European integration, in the area of knowledge, with knowledge goods, capital, services and workers circulating and contributing to making Europe one of the most competitive economies in the world. We see this systemic conversion of supranational policy cooperation as one geared towards ‘knowledge’ as an example of how ubiquitous the act of knowledge alchemy has become in contemporary governance. The EU member states supported the idea of a common space for research. While many of the European Research Area activities were to be implemented through the Open Method of Coordination, which organized cooperation based on the principle of voluntary exchange (for example, guidelines and timetables for reaching common objectives, sharing of best practices, and periodic monitoring for mutual learning), other policy sectors have also been activated. Most notably, the European migration policy sector has been used to achieve European Research Area policy objectives. In the EU context, this is significant because central institutions such as the European Commission have regulatory competences in the migration policy sector to push forward the integration process. By contrast, sensitive policy areas such as higher education have been in the hands of the member states. Thus, by framing efforts to creating the European Research Area as similar to those resulting in the Single Market, the European Commission was able to tap into the modus operandi of the Single Market, which goes beyond the Open Method of Coordination to include the Community Method that places more decision-​making power in the hands of European institutions. 123

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The various policy instruments adopted to realize the European Research Area reflect the multiple governance methods used. For instance, the European Charter for Researchers and the Code of Conduct for the Recruitment of Researchers builds on the principles of Open Method of Coordination (Chou and Real-​Dato 2014) via the Human Resources Strategy for Researchers (Chou 2014; 2016b). By contrast, the Scientific Visa used the Community Method to introduce a measure that would enable the European member states and research organizations to bring in non-​EU nationals (so-​called Third Country Nationals) to the Union (Chou 2012; Cerna and Chou 2014). The recast of the Scientific Visa and the Student Visa into one EU directive instead of two separate measures is a recognition that post-​study pathways are also significant in attracting and retaining foreign talents (Cerna and Chou 2022). What these policy instruments all have in common are their aim to facilitate academic mobility within Europe, and attract those talented researchers outside of Europe to see the continent as an academic destination (cf Chapter 5). As we shall see in this chapter, we find similar policy efforts in other policy sectors and in other world regions and countries. The construction of the Europe of Knowledge also coincided with revisions of the European university model based on the Humboldtian tradition through the Bologna Process and aspirations for economic competitiveness (Nybom 2003; Reinalda and Kulesza 2006; Ash 2008; Michelsen 2010; Paletschek 2011). The global university rankings have further contributed to this process by shaping the policy problem of ‘European higher education’ and helping to identify political and institutional responses to it (Erkkilä 2014). In this framing, European higher education is seen as a policy problem that needed to be solved. One source of this problem identification stems from European universities being compared with American universities and Asian higher education institutions that are rapidly rising in the global university rankings. In this comparison, most European universities were found wanting simply because only a few were present in the league tables of ‘world-​class universities’. This state of higher education in Europe only strengthened policy concerns as time evolved seemingly without policy intervention. In 2005, the European Commission cited the Shanghai and THE rankings that showed European universities performing poorly in global comparisons vis-​à-​vis universities in the US and Asia (European Commission 2005b). At the same time, European universities’ ‘success’ in the global rankings was linked with the notion of economic competitiveness (European Commission 2005a). This came with a shift in the ideas of accountability, where the modernization of European universities is linked to their increasing responsibility for national economy through research output and effectiveness (European Commission 2005b, 9; 2006). Broadly speaking, the rankings have been used by the European Commission both as indicators for the problems 124

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in higher education in Europe, but also as active tools for reform towards objectives set out in the Lisbon Strategy (European Commission 2008; 2009). This is apparent in the European Commission’s communication on ‘supporting growth and jobs –​an agenda for the modernization of Europe’s higher education system’ (European Commission 2011): [T]‌oo few European higher education institutions are recognised as world class in the current, research-​oriented global university rankings. … There is no single excellence model: Europe needs a wide diversity of higher education institutions, and each must pursue excellence in line with its mission and strategic priorities. With more transparent information about the specific profile and performance of individual institutions, policy-​makers will be in a better position to develop effective higher education strategies and institutions will find it easier to build on their strengths. (European Commission 2011, 2–​3) In short, EU policies on the ‘modernization’ of higher education are shifting towards an increasing use of rankings with a double logic, where the existing rankings show too few top higher education institutions in Europe. The argument goes that Europe needed more transparency –​that is, rankings –​ to tackle this problem. To summarize, the increasing emphasis on academic mobility is joined with the new ideas of ‘accountability’ towards ‘society’, resulting in processes of individualization and autonomization in European higher education (Erkkilä and Piironen 2015). Academic performance of institutions is now perceived to rest on individual talented scholars and scientists who roam from one institution to another in the Europe of Knowledge, researching and innovating, but also teaching. The European Commission sought to set the policy agenda by reconceptualizing the role of university in Europe: as both a policy problem (it is the site of European higher education, which the rankings depicted as ‘lagging behind’ those in the US and in Asia), and a policy solution (it is where research is generated, and innovation occurs). To address the policy problem and remove administrative barriers to free movement of knowledge, the European Commission introduced funding programmes and adopted measures such as European Charter for Researchers, the Scientific Visa and the Student Visa for Third Country Nationals. At the same time, the academic institutions themselves are granted more financial ‘autonomy’ (Piironen 2012; Holmén 2022) that then serves as an argument for their responsibility over national economic performance, measured through their ranking success. What the case of the Europe of Knowledge reveals is that the processes of knowledge alchemy are rather messy. Indeed, the ‘talent’ competition imaginary has resulted in 125

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multiple initiatives being activated and drawn from several policy sectors. Interestingly, the ‘talent’ competition imaginary has only encouraged Europe to be more active in other policy sectors that may ultimately contribute to its global ‘success’.

European technological sovereignty and global competition: artificial intelligence regulation, governance and data The EU has profiled itself as the global regulator of the big technological corporations. Such regulative governance is also linked to European identity politics and promoted through historical references to (post-​)World War Europe. In March 2019, French president Emmanuel Macron’s address for ‘European renewal’ contains a collective ‘European’ positioning calling for action in global competition. In his speech, he draws a gloomy ‘automated future’, with references to sleepwalking (to war), populism, extremism and people who are about to lose control (Macron 2019). Instead of (medieval) cities, (Popperian) open society and competition between like units, this future trajectory appears as the final frontier of public–​private boundaries in knowledge governance with Europe as a regulatory superpower vis-​à-​vis the US and China (the innovators). Following Macron’s public address in 2019, the newly appointed European Commission headed by Ursula von der Leyen adopted a similar position on European competitiveness and AI.1 Sharing Macron’s hierarchical worldview, the European Commission defined itself as a ‘geopolitical commission’ in the digital age, carrying strong economic connotations (European Commission 2019c, 2; von der Leyen 2019). While global indicator producers highlight cities as the motors of innovation, the European Commission projected that competition is at the macro-​regional level, with Europe being pitted against the US and Asia. The geopolitical perspective pervades throughout the European vision of innovation, digitization and automation. The European Commission refers to ‘technological sovereignty’ as a form of control that can still be achieved in the global competition, also ensuring European ‘ownership’ of outputs (European Commission 2019b, 4, 5; 2019d, 13; von der Leyen 2019, 9). The European approach to competitiveness, digitization and AI sees Europe as a global standard-​setter. Here the EU is projecting its global success 1

The EU is by no means alone in anticipating future in innovation governance. The OECD’s project on AIG highlights possible futures and stresses uncertainty in predicting future events (OECD 2020a). While the OECD’s approach bears similarities in its emphasis of Grand Challenges and ‘crisis’, it does acknowledge that anticipation should concern different scenarios, not only those that are regarded likely or desirable. 126

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in regulating privacy through the General Data Protection Regulation to the quest of regulating AI, seen as a potential competitive advantage for Europe (European Commission 2019d, 4, 13, 17; von der Leyen 2019, 7, 9). The European framework for AI is largely a value-​based ethical project, but it is nevertheless framed in terms of global competitiveness (European Commission 2018a, 3; 2019a, 1, 3; European Council 2018, 3, 4). This example explicitly highlights how knowledge alchemy is also very much a value-​creation exercise, where certain values (in this instance, ‘European’) are elevated above others as more preferable. The EU is perceived as a ‘champion’ of global norms attempting to establish an ethical, ‘human-​centric’ and legal approach on AI (European Commission 2018a, 3; 2018b, 8; 2019a, 2, 8, 9; 2019c, 4; 2019d, 13, 17; European Council 2018, 3). While ‘technological sovereignty’ and Europe’s own action are central in the envisioned regulatory scheme, the European Commission –​unlike global indicator producers –​prefers multilateralism and global standard-​setting in digital matters (European Commission 2018a, 3, 19; 2018b, 8; 2019c, 4). At the same time, it has also expressed its willingness to take a lead in regulation if necessary (European Commission 2019c, 4; 2019d, 12, 17).2 Developing global norms should thus be aligned with European values that make the foundation of the ‘European path’ (European Commission 2018a, 3; 2018b, 8; European Council 2018, 3; von der Leyen 2019, 9). The EU’s ambition to become a global regulator overshadows its attempt to become a global innovator. The mission statement of the ‘geopolitical’ European Commission sees Europe as trailing North America and Asia in critical investments in innovation (European Commission 2019c, 2; 2020, 5; von der Leyen 2019, 7). Acknowledging that some areas of competition such as consumer platforms may be already lost, the European Commission seeks to concentrate its investments to sectors where the EU still has a credible chance of being a ‘global champion’ (European Commission 2020, 6). While the notion of ‘skills’ does not feature prominently in the EU’s rhetoric, digital skills –​owing to formal education and informal learning –​ are emphasized and new programme and investments to update skills are proposed (European Commission 2018b, 5; 2020, 6). Mass unemployment is a less pronounced concern than for instance in the assessment of the World Bank (cf World Bank 2019). Europe is seen to need ‘disruptive firms’ in global competition but also be able to maintain internal stability by managing instabilities in labour markets (European Council 2018, 3, 4, 5; European Commission 2019d, 4). Investments in new innovation hubs and centres of excellence in research are called for, yet the ability to ‘attract’

2

As key arenas of global AI discussions, the G7, G20, OECD, World Trade Organization and United Nations are named (European Commission 2018a, 19, 8; 2020, 8). 127

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and ‘retain’ skills is reduced to a vision of a European ‘lighthouse centre of research and innovation for AI’ (European Commission 2020, 3–​4, 5–​6). This again indicates a very centralized perception of innovation, where a single institution of excellence has trickle-​down and spill-​over effects for the whole of Europe. Altogether, the European perception of the global competition and digital innovation is hierarchic (European Commission 2019c, 2; von der Leyen 2019, 7). It assumes an out-​of-​control technological sphere that threatens European sovereignty and needs to be regulated already at the root level, albeit from a top-​down approach. In the rhetoric, the concepts of ‘sovereignty’ and ‘geopolitics’ are linked to notions of ‘excellence’ and ‘trust’. The ‘European values’, largely undefined, are the normative yardstick that is underlying the European initiatives. While the point of departure has been to gain control over the technological development and regulate the use of personal data of Europeans, it is then ironic that the European Commission proposes that in order to become competitive, Europe needs to become a ‘global hub for data’ (European Commission 2020, 25). This implies commodification of public sector information resources, including health data, by opening them for ‘re-​use’ as ‘raw material’ in AI applications in accordance with ‘European values and rules’ (European Commission 2018a, 3; 2020, 3). While public sector information has been a European policy issue already since the late 1990s (European Commission 1998), the aim to become a global data hub is paradoxical against the background that the European Commission has profiled itself through regulating privacy issues globally (that is, through the General Data Protection Regulation). Handing out public data for private use stands also in contradiction with the idea of maintaining ‘sovereignty’, as the modern sovereign states are fundamentally based on their exclusive possession of information and registry data (Desrosières 1998; Sheehan 2006). The future narratives downplay the possible contradictions in the European Commission’s plans. The European Commission sees AI applications as spelling fundamental changes to society and economy (European Commission 2019d, 13), but the future of automation and AI is framed in positive terms, transforming ‘our world for the better’ and benefiting ‘the whole of society and economy’ (European Commission 2019a, 1; 2020, 25). In a speech before the European Parliament, Ursula von der Leyen argues that automation that can free humans to focus on ‘empathy and creativity’, though on the condition that the EU secures its position as a geopolitical actor (von der Leyen 2019, 6, 8, 9). This future vision is portrayed on a continuum with the peaceful Velvet Revolution, delivered with quotes from, and references to, Václav Havel (von der Leyen 2019, 4, 14). Here the history of Cold War and the collapse of communism is again evoked, stressing the great junctures of European post-​war history. Compared with Emmanuel 128

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Macron’s dystopian visions of Europe ‘sleepwalking’ into its demise, we have moved to a more positive scenario, where the Europeans have the chance to gain control and prevail in their struggles against the imagined oppressors. It is interesting to note how the ‘European’ approach on technological sovereignty also emphasizes competitiveness in its attempt to promote the EU as a global regulator of AI. Similar to the strategies of the index producers, the EU also stresses the ability of innovation ‘hubs’ to attract, retain and exploit ‘talent’. There are also interesting parallels with the imaginaries of the index producers discussed in Chapter 4, including in the historical narratives, now also emphasizing peaceful ‘revolutions’ and ‘open society’. In the next section, we turn to China and its transregional initiatives to examine how it seeks to manifest its talent competition strategies under the umbrella of the Belt and Road Initiative. Similar to the European emphasis on openness, China also wants other partner countries and institutions to be open to the Chinese way of state-​to-​state and institution-​to-​institution collaboration.

China’s knowledge governance via the Belt and Road Initiative The increased prominence of China on the global stage has moved the Middle Kingdom into the position of being a country where its developments, institutions and initiatives in the economic and knowledge sectors are now natural units for comparison. This has been made most noticeable following the launch of the Belt and Road Initiative in 2013. President Xi Jinping announced the ambitious Belt and Road Initiative as a constantly evolving and expanding infrastructure-​driven inter-​regional connectivity programme. The Belt and Road Initiative has grown in both scope and depth since it started: originally involving 65 countries, the Belt and Road Initiative now includes over 150 partner countries in five areas of connectivity (that is, policy coordination, facilities connectivity, unimpeded trade, financial integration, and people-​to-​people connectivity). Some examples of connectivity include the ‘Digital Silk Road’, ‘Arctic Silk Road’ and, in the COVID-​19 pandemic context, the ‘Health Silk Road’. The sprawling nature of the Belt and Road Initiative has come to represent China’s overseas investment, making it difficult to clearly delineate the Belt and Road Initiative’s boundary from other areas where China has declared an interest. The size of the initiative and its coverage has attracted tremendous interest from practitioners and scholars alike, with the latter focusing on the geopolitical and geoeconomic roles of China through the Belt and Road Initiative. These debates have revolved around two grand narratives concerning China’s role. The first narrative depicts the Belt and Road Initiative as a shared economic development between China and its partner countries. It is a narrative invoking the development of partner 129

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countries and China via connectivity, and a narrative that Beijing has embraced as indicative of its true motives. The second narrative portrays the Belt and Road Initiative as an instrument of power that China wields to promote its ascent on the international stage. Here, connectivity translates to opening the door to China and inviting it to enter. This narrative emphasizes the geopolitical dimension of the initiative, and resonates with the views expressed from Washington and its ally capitals. For us, the Belt and Road Initiative is especially insightful to understanding how China has chosen to respond to the ‘talent’ competition imaginary. To draw this out, we examine university alliance-​building efforts through the initiative and show how knowledge alchemy is equally at work in China’s knowledge governance activities. The knowledge sector is integral to at least one of the ‘five connectivities’ –​ ‘people-​to-​people’ –​but it has thus far been overlooked and less analysed in comparison to other sectors (exceptions include Cabanda et al 2019; Peters 2020; van der Wende et al 2020). While China is a newcomer to contemporary university alliance-​building, it has spearheaded some of the newest alliances. In their research, Feng and Gao (2020, 109–​111) identify three phases of developing international university consortia along the New Silk Road: before 2000 (Phase 1); 2001–​2020 (Phase 2); and after 2011 (Phase 3). Feng and Gao (2020, 104) found 20 university consortia along the New Silk Road throughout the three phases, and many have been created after the Belt and Road Initiative’s formal launch in 2013: ‘University Alliance of the Silk Road’ (UASR) in May 2015; ‘Belt and Road Initiative University Alliance’ in October 2015; ‘University Alliance of Belt and Road Deans’ in 2015; ‘Asian Universities Alliance’ (AUA) in April 2016; ‘Alliance of Belt and Road Business Schools’ in August 2017; ‘University Consortium of the 21st Century Maritime Silk Road’ created in October 2018; ‘International Alliance of the Belt & Road Engineering Education’ in November 2018; and the ‘Alliance of Belt and Road Environmental Deans’ in December 2018. An official list of all university alliances launched within the context of the Belt and Road Initiative is currently not available, but it is clear that the proliferation of China-​led3 university alliances is widespread since the formal launch of the initiative. The extent to which these university alliances sustain collaboration between partners over time is an open question. Indeed, for those interested

3

The University Alliance of the Silk Road and the Asian Universities Alliance are not spontaneous initiatives launched by the founding Chinese universities. These alliances are state-​driven, with funding resources coming from the state. Flagship universities located in Asia often work closely with the state, so China and our two university alliance case studies are not exceptions. 130

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in university alliance-​building in the context of the Belt and Road Initiative, there is a more fundamental challenge: the existence of publicly available information about most of these networks. For instance, the only reference we have on the Belt and Road Initiative University Alliance comes from a public statement on the website of Lanzhou University, a founding member; very little information is available on many of these university alliances. Indeed, the only publicly accessible data are those concerning the University Alliance of the Silk Road and the Asian Universities Alliance. Even with limited data, we show how, examining the basic organizational features of these two alliances (that is, their membership and criteria for joining, governance network structures, and alliance activities), we are able to tease out how China has responded to the ‘talent’ imaginary that echoed the ‘development via connectivity’ and ‘geopolitics’ strategies discussed earlier. In May 2015, Xi’an Jiaotong University led the launch of the University Alliance of the Silk Road to improve communication and collaboration among the universities situated in the Silk Road Regions (UASR 2015a). Established as a non-​governmental, non-​profit and international cooperation platform for higher education institutions, the University Alliance of the Silk Road intends to provide a space for member universities to exchange ideas and become partners on various projects, while also linking members and non-​members for collaborative discussions. The membership of the University Alliance of the Silk Road has grown since its creation: more than 155 universities are member4 institutions and some 19 universities are observers,5 representing more than 38 countries around the world (UASR 2015a). The members of University Alliance of the Silk Road include many types of higher education institutions, from those offering focused training (such as in pharmacy in the case of Tashkent Pharmaceutical Institute, Uzbekistan) to more comprehensive research universities (for example, University of Liverpool, UK). The diversity of institutions is a defining feature of the University Alliance of the Silk Road. Less than a year later, in March 2016 at the Boao Forum for Asia,6 Tsinghua University took the lead in launching the Asian Universities Alliance, which was formally established in April 2017 (AUA 2019). Created to improve the accessibility of education resources among the alliance members, the Asian Universities Alliance aimed to foster an ecosystem for innovative collaboration

4

5

6

The member institutions are listed at http://​uasr.xjtu.edu.cn/​Abo​ut_​U​ASR/​Memb​ers. htm (Accessed 1 February 2022). The observer universities are listed at http://​uasr.xjtu.edu.cn/​Abo​ut_​U​ASR/​Observ​ers. htm (Accessed 1 February 2022). The Boao Forum for Asia is a high-​level annual forum for heads of state and government of 28 Asian countries to meet and discuss economic integration. The importance of the platform has been compared to the WEF in terms of agenda-​setting. 131

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to address regional and global challenges. The Asian Universities Alliance’s membership has remained unchanged since its founding –​15 universities –​ and includes the most well-​known comprehensive research universities in Asia: Chulalongkorn University, The Hong Kong University of Science and Technology, Indian Institute of Technology Bombay, King Saud University, National University of Singapore, Nazarbayev University, Peking University, Seoul National University, Tsinghua University, United Arab Emirates University, Universitas Indonesia, Universiti Malaya, University of Colombo, University of Tokyo, and University of Yangon (AUA 2017a). Many of these universities are flagship universities in their countries, and are often given resources to compete globally. The membership criteria for joining the University Alliance of the Silk Road and the Asian Universities Alliance are distinct, and they are revealing of whether the alliance is open or exclusive. For the University Alliance of the Silk Road, any institutions offering ‘bachelor degree or higher degree education’ are eligible (UASR 2015b, Article 5). The membership application for the University Alliance of the Silk Road consists of two pages and is available online.7 The information the prospective members are asked to submit are straightforward and are generally accessible on existing university websites. Beyond the basic details about the applicant institution (for example, name, address, website, logo, institutional classification, degrees awarded, and legal representative), the application asks the potential member to provide a self-​introduction of 300–​500 words and to indicate the fields of cooperation in which it intends to engage. Once the secretariat accepts the application, the applicant university may become an observer and access organized activities and conferences. During the annual Executive Council conference, members individually evaluate and vote on received applications. An applicant university becomes a full member of the University Alliance of the Silk Road when its application receives two-​thirds votes in favour. The open nature of the University Alliance of the Silk Road invites questions concerning the purpose of this alliance with regards to the ‘talent’ competition imaginary. Membership to the Asian Universities Alliance is highly selective. Five distinct criteria are listed: 1. geographical (potential member must be located in Asia); 2. contribution intention (applicant institution must recognize and embrace the mission of the Asian Universities Alliance, and a demonstrated capacity and willingness to host future activities and events);

7

Application and instructions are available at http://​uasr.xjtu.edu.cn/​Abo​ut_​UA ​ SR/​Mem​ bers​hip_​Appl​icat​ion.htm (Accessed 1 February 2022). 132

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3. representation (potential member must be a leading institution in its country or region); 4. socially responsible (applicant institution must seek to contribute to regional and global issues, and the advancement of mankind); and 5. expansion limitation (no more than two member institutions per country or region) (AUA 2017b). A higher education institution interested in becoming a member cannot apply directly; at least one existing founding institutions must nominate the applicant for membership by submitting a recommendation letter to the secretariat (AUA 2017c). The board of the Asian Universities Alliance would assess the potential member institution following the five criteria and only after its presidency and executive presidency approve the nomination. An applicant institution becomes a member when two-​thirds of existing active voting members support its nomination. The exclusive nature of the Asian Universities Alliance suggests that the purpose of this alliance is to enable partner universities to ‘connect’ with each other in order to compete globally. The overall governance structures for University Alliance of the Silk Road and Asian Universities Alliance are similar. Both alliances have an executive body that is in charge of all activities, and a permanent secretariat that oversees all communication, event and meeting organization, and other tasks relevant to founding alliance activities. As the Chinese universities that initiated the alliances, Xi’an Jiaotong University is the secretariat for University Alliance of the Silk Road and Tsinghua University for the Asian Universities Alliance. What differentiates the governance structures between the two alliances are slight variations in the composition of their executive bodies and their term duration. Both the University Alliance of the Silk Road and Asian Universities Alliance constitute platforms for member universities to engage in high-​level strategy and policy developments. In the case of University Alliance of the Silk Road, this takes place through its annual presidents’ forums, where themes of significance for its member universities, such as ‘challenges and cooperation opportunities in the digital era’, are discussed, and Executive Council meetings, where key decisions concerning its operations (such as membership applications) are made (UASR 2020, 11–​16). We find a similar format in the Asian Universities Alliance. The University Alliance of the Silk Road and Asian Universities Alliance carry out similar activities –​but in different configurations, frequency and intensity –​that could be broadly categorized as: exchange; research collaboration; and high-​level strategy and policy developments. Exchange activities constitute the bulk of activities for both alliances. For the University Alliance of the Silk Road, three kinds of exchanges are organized: ‘inter-​ university’, ‘talent nurturing’ and ‘cultural’ (UASR 2015b, Articles 14, 15, 17). While being an open alliance, it is clear that the members of the University 133

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Alliance of the Silk Road are also concerned with ‘talent’. Examples of programmes under the University Alliance of the Silk Road all have ‘Silk Road’ in their names: ‘Silk Road Youth Leadership Program’, ‘Silk Road Robotics Innovation Competition’, ‘Silk Road International Summer Schools and Summer Camps’ and ‘Silk Road Art Festival Gala’ (UASR 2020, 17–​22). The strong branding approach ensures that there is no confusing these China-​ supported initiatives with those originating from elsewhere. While covering a similar range of activities, the exchange programmes of the Asian Universities Alliance are organized by target groups: students and university administrators (AUA 2021a). Under student mobility, the Asian Universities Alliance coordinates the following: the Asian Universities Alliance Youth Forum, Asia Deep Dive Programme, Overseas Study Programme, and Arts and Sports Events (AUA 2019, 12–​29). The majority of these programmes are focused on nurturing young ‘talents’ who are nominated or chosen by partner universities. The comparably smaller institutional membership (15 universities) also translate to these programmes being more exclusive, with only a limited number of young ‘talents’ being able to attend the programmatic activities. Looking at activities under university administration, the Asian Universities Alliance facilitates the following: the Asian Universities Alliance Staff Exchange Program (AUA 2019, 34–​36) and University Administration Meetings (AUA 2021a). Similar to those programmes nurturing young ‘talents’, the participating university administrators are selected as part of skills training and intercultural exchange. Research collaboration is organized differently in both alliances. According to Article 20 of University Alliance of the Silk Road’s Charter, members are encouraged to establish ‘diverse regional and specialized sub-​alliances’ to promote collaboration between universities on discipline-​based topics and issues (UASR 2015b). There are at least 11 sub-​alliances focusing on topics such as health, energy, law, advanced manufacturing, management, tourism, intellectual property, forensic medicine and engineering (chemical, mechanical and aerospace) (UASR 2020, 31–​38; 2021). At least seven sub-​ alliances are led by respective Xi’an Jiaotong University schools (for example, the School of Aerospace Engineering initiated the Mechanical and Aerospace Engineering Sub-​Alliance) and include non-​alliance members (for example, the National University of Singapore, University of Helsinki and University of Bergen are members of the Silk Road Law School Alliance) (UASR 2021). While the sub-​alliance format promotes research collaboration, talent cultivation and nurturing are also explicitly part of sub-​alliance activities. For the Asian Universities Alliance, research collaboration takes place through these formats: the Asian Universities Alliance Scholars Award Programme, Academic Conferences, and Joint Research Programmes (AUA 2021a). The Asian Universities Alliance promotes the Scholars Award Programme as a ‘flagship project’, facilitating the research stays of 134

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10–​14 days for 60 faculty members in 2020–​2021, 59 in 2019–​2020, and 29 in 2018–​2019 at partner institutions (AUA 2019; 2021b). The small size of the cohorts suggests targeted resources. Under Academic Conferences, members of the Asian Universities Alliance have hosted five events since 2018 on basic research, theoretical innovation, and AI (Tsinghua University, for postgraduates), water (University of Yangon), water management and climate change (Bangkok, through the ASEAN Academic Networking in Water, Disaster Management and Climate Change framework), mass culture communication (Peking University), and sustainable universal health care coverage (Chulalongkorn University) (AUA 2021c). In December 2020, the United Arab Emirates initiated the Joint Research Program to fund research between a Principal Investigator (PI) from the United Arab Emirates University and a Co-​PI from at least one member university of the Asian Universities Alliance (AUA 2021d). Successful grant proposals are funded for two years, with a maximum budget differentiated between laboratory-​based research ($135,000) and non-​laboratory research ($68,000) (AUA 2021d). The comparison between the University Alliance of the Silk Road and Asian Universities Alliance tell us the following about how China has responded to the ‘talent’ competition imaginary through university alliance-​building efforts under the Belt and Road Initiative. First, we see that China initiates overlapping university alliances because it benefits from an increased network density and network relationship (the ‘many-​to-​many’ principle). Second, we find that China differentiates between the networks it initiates; it pursues different objectives and types of activities within the two alliances, and thus rejects the ‘one-​size-​fits-​all’ principle. This is important to highlight because it reflects a commitment to knowledge alchemy while still engage in searching for the ‘right’ formula. Indeed, the University Alliance of the Silk Road represents an open, inclusive and participatory framework of cooperation, and the Asian Universities Alliance is an exclusive, hierarchical and China-​led network. China and its higher education institutions are certainly not the only ones keen to compete in the global talent race. In the next section, we turn to city-​level strategies to explore how cities are now emerging as global players in this competition.

Constructing the hub: city strategies As discussed in Chapter 4, the imaginaries on competitiveness by the global indicator producers now identify cities as key actors engaging in a race for human capital or ‘talent’. To foster competitiveness, governments and international organizations are emphasizing the role of innovation hubs and academic institutions (European Commission 2011; New Zealand Government 2012; Mazzarol 2013; Turnbull 2015; Science Foundation Ireland 2015; UK Government 2015; World Bank 2015). This also challenges the role of states as the key actors in economic competitiveness. Owing to 135

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global rankings, small and open economies –​such as the Nordic countries and Singapore –​have become idealized models for other countries (Blakely 2017; Blanc-N ​ oël 2018; Hunt 2018). The higher education institutions and cities are also subsumed to economic competition, conflating urbanization with competitiveness (Kangas 2017). Table 6.1 shows how global indicators of competitiveness and innovation now portray selected countries and innovation hubs. The case selection is partially based on a previous study (cf Erkkilä and Piironen 2018), focusing on smaller states from different geographic regions, leaving out federal states and larger nations for the sake of comparability. Moreover, the focus is on countries with clearly identifiable dominant innovation hubs that rank in the current measurements to allow comparisons between different measures. Their scores in the different rankings are strikingly similar. On the one hand, this is in line with the argumentation of knowledge producers, who often verify their results with high correlations against other indicators. On the other hand, these rankings share ontologies, conceptual frames and methodology, which makes the ranking scores, which are similar, understandable. Moreover, there is an overlap in concepts, methods and data sources (see Chapter 2), which, unsurprisingly, also accounts for the similarities in the ranking scores. Looking at the strategy documents of cities, there are great similarities with the policy scripts on world-​class university and global talent competition discussed in Chapters 2, 3 and 4. For example, Stockholm’s Vision 2040, ‘A Stockholm for Everyone’, presents the Stockholm area as a ‘leading knowledge region’ with references to global competition, rankings, talent and infrastructure, as well as openness. Stockholm is one of the world’s leading knowledge regions. The city is famous for its wide range of educational courses and for its highly skilled and creative residents, with excellent language skills and a unique living environment. The university colleges of Stockholm are an accommodating and attractive choice for everyone. The students at the region’s university and colleges are in high demand on the global labour market, and have the skills and qualifications they need to achieve their dreams. … The Stockholm region has risen in the rankings in a variety of areas of research, and has worked to develop new, multidisciplinary research partnerships. The region is a world leader in areas such as information technology, the creative professions, life sciences, environmental technology, social sustainability and human rights. Close collaboration between the business community and high-​ class international research facilities have made Stockholm attractive to businesses and talents in all sectors. The region is distinguished by its great openness to new ideas and individuals. (Stockholm 2017, 19) 136

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Table 6.1: Rankings of selected countries and innovation hubs World Global Competitiveness Competitiveness Ranking (2019) Index (2019)

Global Global Innovation Power City Index Index (2019) (2019)

Global Talent Competitiveness Index (2019)/​ Countries

Global Talent Competitiveness Index (2020)/​ Cities

Academic Ranking of World Universities (2019)

Type

National competitiveness

National competitiveness

Innovation City capacity of magnetism nations

Countries’ ability Cities’ ability to Ranking of universities’ research to attract and attract and retain performance retain talent talent

Netherlands (Amsterdam)

6/​63

4/​141

4/​129

6/​48

8/​125

20/​155

101–​150/​1,000 University Amsterdam 101–​150/​1,000 University of Amsterdam

Denmark (Copenhagen)

8/​63

10/​141

7/​129

20/​48

5/​125

15/​155

26/​1,000 University of Copenhagen

Hong Kong SAR, China (Hong Kong)

2/​63

3/​141

13/​129

9/​48

n/​a

6/​155

101–​150/​1,000 The Chinese University of Hong Kong 101–​150/​1,000 The University of Hong Kong 201–​300/​1,000 City University of Hong Kong 201–​300/​1,000 The Hong Kong University of Science and Technology 201–​300/​1,000 The Hong Kong Polytechnic University 701–​800/​1,000 Hong Kong Baptist University 701–​800/​1,000 The Education University of Hong Kong

(continued)

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Ranking

newgenrtpdf

Table 6.1: Rankings of selected countries and innovation hubs (continued) World Global Competitiveness Competitiveness Ranking (2019) Index (2019)

Global Global Innovation Power City Index Index (2019) (2019)

Global Talent Competitiveness Index (2019)/​ Countries

Global Talent Competitiveness Index (2020)/​ Cities

Academic Ranking of World Universities (2019)

Singapore (Singapore)

1/​63

1/​141

8/​129

5/​48

2/​125

3/​155

67/​1,000 National University of Singapore 73/​1,000 Nanyang Technological University 801–​900/​1,000 Singapore University of Technology & Design

Sweden (Stockholm)

9/​63

8/​141

2/​129

14/​48

7/​125

18/​155

36/​1,000 Karolinska Institute 73/​1,000 Stockholm University 201–​300/​1,000 KTH Royal Institute of Technology 401–​500/​1,000 Stockholm School of Economics

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Ranking

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The Stockholm strategy (published in 2015) also emphasizes the notion of the ‘smart city’, aiming to be ‘the smartest city in the world’ with leading technological infrastructure and educational institutions collaborating with the city in an orchestrated fashion where ‘results from research are used to develop the city’s operations, and experience is then ploughed back into research’ (Stockholm 2017, 19–​20). The earlier strategy –​Vision 2030 –​ aimed for a ‘World-​Class Stockholm’.8 Similar accounts can also be found in the strategies of other cities. Copenhagen also has a specific Science City Strategy titled ‘Towards World Class Innovation District’ (Copenhagen Science City 2018). Copenhagen’s Business and Growth policy talks directly about ‘attracting’ and ‘retaining’ talent (City of Copenhagen 2015, 3, 6, 20–​21), concepts used in the GTCI (see Chapter 3). Talent in this context is then further identified as ‘international knowledge workers’ and the City of Copenhagen launched an initiative to ‘[i]‌ncrease the retention of international knowledge workers by offering help to find jobs and job match, introduction to cultural-​and leisure offers, events and help to the establishment of social and professional networks for both students, employees and accompanying spouses’ (City of Copenhagen 2015, 21). Interestingly, this highlights similar mundane everyday issues in Danish context that we have observed in the case of Singapore (see Chapter 5). In the context of the Netherlands, Amsterdam provides an interesting case for a global city that after Brexit has also risen to compete with London as a financial centre (Fleming et al 2022). It is noteworthy that in the Amsterdam municipal government’s agreement of the governing coalition hardly any references were made to ‘talent’ or ‘knowledge’ (Amsterdam 2018). This 79-​page document, addressing the city residents, contains only three occurrences of the term ‘knowledge’, in the context of cultural marketing, business development (facilitating ‘knowledge parks’) and cooperation between city and city districts (with the help of ‘knowledge institutions’). The notion of talent has only four occurrences, in the context of the culture and sports. But the City of Amsterdam’s strategies on economic policy, clearly targeted for an international audience, carry strikingly different conceptualizations of ‘talent’ and ‘knowledge’, promoting Amsterdam as the ‘city of knowledge’ (Amsterdam 2022a). This strategy is focused on

8

It is also interesting to note that already in 2009 Stockholm University shows reflexivity over the rankings and states as its explicit strategic goal to ‘improve its position in the leading ranking lists, naming Shanghai and Times HE explicitly’ (Stockholm University 2009, 19). This is linked to increasing competition over talent –​best students, teachers and researchers. 139

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three areas: developing talent; knowledge and innovation; and research and valorization. The policy area of knowledge development contains the goal of ‘expanding the city’s knowledge base’ that closely resembles one formula of talent competition: Increasingly, businesses in the Netherlands and abroad are choosing to settle in areas that offer a large pool of highly skilled talent. To ensure continued economic growth, it is vital that Amsterdam and the greater Amsterdam Metropolitan Area remain competitive at both regional and international levels. This can be accomplished by [d]‌eveloping and strengthening talent as effectively as possible[,] [f]ocusing on attracting and retaining international talent [and] [p]romoting Amsterdam as an innovative knowledge centre. (Amsterdam 2022b) Capitalizing on knowledge is defined as ‘valorization’, tying the knowledge institutions to Amsterdam’s success and prosperity: Making effective use of knowledge will boost Amsterdam’s position as a centre of innovation and ensure our success in the long term. The solution lies in forging close ties between educational institutions, research centres, the government and the business sector so that scientific knowledge can be disseminated more quickly among Amsterdam’s businesses and residents. Capitalising on knowledge in this way is known as ‘valorisation’. (Amsterdam 2022b) In the Asian context, Singapore and Hong Kong are also exploring formulas of talent competition. Hong Kong’s 2030+​strategy presents a vision of ‘a liveable, competitive and sustainable “Asia’s World City” ’. Knowledge governance is prominently present in the strategy that aims to ‘foster an enabling environment for innovation and technology development and create a new momentum for economic growth’, through developing ‘an international innovation and technology hub in the [Greater Bay Area] with Hong Kong as a gateway’ (Hong Kong 2016, 17). This also comes with the aim of providing ‘conducive environment for enhancing and optimising human capital’ while improving ‘Hong Kong’s liveability to attract and retain talents’ (Hong Kong 2016, 18). Not only is this standard approach on constructing innovation ‘hubs’, but it also duly repeats concepts that are present in the governance metrics –​‘enabling innovation’, ‘attracting and retaining talent’, ‘liveability’ (see Chapter 3). Singapore also actively engages in the race to be the leading Asian innovation hub. Singapore’s Research Innovation Enterprise Plan 2020 emphasizes research excellence in a straightforward manner as ‘strong base of scientific 140

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capabilities and a pipeline of ideas that can drive the next phase of growth’ (Singapore 2016, 30). In a similar fashion, the Enterprise Singapore Strategy identifies developing human capital as a strategic goal for ‘growing the talent pipeline for Singapore enterprises’ (Enterprise Singapore 2019). ‘Talent’ is also a marker for various concrete programmes, schemes and priorities of the Research Innovation Enterprise Plan 2020 (Singapore 2016, 17, 18, 23, 35, 38). At the same time, the plan echoes the same kind of normative sentiments as the EU’s strategy on AI, associating research and innovation with competitiveness, leading to a harmonious and prosperous future: In a competitive world changing rapidly with technological advances, our research, innovation, and enterprise efforts will be crucial to bringing Singapore and Singaporeans forward in the next stage of our development. We must continue to invest in science and technology to support growth and innovation, and exploit new knowledge to improve our standard of living, and diversify and create new industries. This will allow Singapore to continue to be a good home with many opportunities, where Singaporeans can pursue their dreams and progress together as one people. (Singapore 2016, 42) As Singapore and Hong Kong both emerge from the COVID-​19 pandemic, they have also revised their talent migration policies to compete with each other. As of January 2023, the Singapore government accepts applications for the Overseas Networks & Expertise Pass (ONE Pass), which has been designed to enable ‘high-​earners’ and ‘achievers’ to move to Singapore without having already secured a job. Promoted as a visa for ‘top talent’, the ONE Pass sets a monthly salary threshold at a minimum of 30,000 SGD (about 21,000 USD and EUR). A month after Singapore introduced the ONE Pass, Hong Kong announced its plans to relax its visa rules, as an effort to bring back foreign talents who have left its territories during the COVID-​19 lockdown. Specifically designed in response to Singapore’s ONE Pass, Hong Kong’s new visa also targets ‘top talents’, again defined by a salary threshold of at least 2.5 million HKD (about 318,000 USD and EUR) per annum. What these examples tell us is that talent competition is fierce for those who are engaged to win. Strategies and policies are visible across governance levels, and they activate measures in multiple policy sectors.

Conclusions In this chapter, we have discussed the strategies and policies concerning talent competition that bring knowledge alchemy to life. Through multiple case studies emerging and unfolding across different governance levels –​from the macro-​regional to the national and transregional, and at the city level –​we 141

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see how the global competition for talent now traverses higher education policies, migration policies, university recruitment practices and partnership strategies, and AI regulation. In the case of the EU, we observed how the presuppositions about the global talent competition have been integrated into its strategies and policies concerning scientific recruitment, funding, big technology and even AI. Here, global talent competition is increasingly associated with anticipations of future AI governance and competitiveness amid increasing automation. We have discussed how cities have become global players in talent competition and how they now evoke very uniform and standardized future aspirations, following universal formulas of largely symbolic nature. Through these twin developments taking place at the macro-​regional and city levels, we see that states have become challenged by both supranational and subnational actors seeking to take a lead. Interestingly, when we look at the case of China and its alliance building efforts through the Belt and Road Initiative, we do not see a ‘new’ strategy at play professing a new imaginary. On the contrary, the two university networks we examined are driven by the ‘talent’ competition imaginary. In these instances, higher education institutions are placed at the centre of the global web of connectivity through differentiated approaches: ‘many to many’ in the case of the University Alliance of the Silk Road, and the elite and highly selective in the case of the Asian Universities Alliance. The goals of both university alliances are simple: to centralize China’s role in the world –​geopolitically, geoeconomically and in knowledge terms. For China, the strategy is one articulated through ‘connectivity’. Knowledge alchemy’s allure is no longer confined only to national actors working in one policy domain but widespread to different levels of governance and actors yearning to be ‘competitive’. The entanglement of multiple rationales that map onto the ‘talent’ competition imaginary suggests that knowledge alchemy is a very messy process, but one in which draws in policy makers and decision-​makers concerned about the overall competitiveness of their institutions, countries and region.

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Conclusion Today, we continue to live in magic. Scientific and technological advancement have indeed lifted our everyday lives to wonderment, but these advances have also introduced magical practices to governance. Our book has been about the magic taking place in knowledge governance, the processes of steering and governing state information, and has been the key to national competitiveness. We referred to this magic as knowledge alchemy, which we defined as a generic process of transforming mundane practices and policies of knowledge governance into competitive ones following imagined global gold standards and universal symbolic formulas. Through this reclassification, an object, be it an individual, higher education institution, city or country, has been integrated into a value production chain, a series of actions (a syntax, script, narrative or storyline) that produced value. In this concluding chapter, we summarize the multiple processes that have ushered in alchemy into knowledge governance, the mechanisms through which these processes have been taking place, and how our book connects with major debates in the social sciences and policy circles about social transformation. The rise of quantification and governance by indicators found a parallel process in automation through machine-​based reasoning. Here, automation refers to a process that involves limited or no human agency in predetermined models and patterns of governance. Societies have been increasingly governed by algorithms, ranging from the digitalization of our daily lives to algorithmic reasoning in market activities and public governance. Quantification and automation depend on and, in effect, feed into one another. Based on logical rules in weighting of different attributes, rankings themselves are algorithms and thus the numerical objectifications of reality embodied in indicators have rendered complex social institutions comparable and suitable for rules-​based reasoning. These developments have strong implications in how we design policies and practices to govern our interactions and tackle global challenges. 143

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Our book argued that governance of innovation, higher education and human capital are being transformed by processes of commodification and quantification that now intersect with automation. For us, automation imposed strong future imaginaries and presupposition of policies based on, interestingly, the past. As a result, institutional practices in key knowledge sectors have been increasingly predetermined using numerical data and algorithms that limited autonomy in decision-​making and eliminated human reflection. This has been taking place in a context of growing local, national and regional competition, with politicians and policy experts uncritically adopting global policy models and trying to turn their local institutional practices to match the international ‘gold standards’ produced by global metrics and benchmarking. This is problematic for governance. Our book introduced and showed how to apply the concept of knowledge alchemy to examine this relationship between commodification, quantification and automation, as well as the institutional outcomes when their coexistence intensifies.

Models and agency in global knowledge governance Our starting point has been the nexus between the political economy of knowledge and the global governance of knowledge, and how this nexus has become and remains a key area of concern for those interested in the relationship between strategies and actual performance in a variety of sectors. While the role of knowledge in contemporary national economic strategies is now accepted as crucial, the ways in which the different models and policy scripts that inform these strategies are constructed and circulated globally are less well-​known. Taking these two distinct modalities as our analytical starting point, we argued that this separation has been highly problematic because of the growing intersection between the parallel processes of commodification, quantification and automation. By commodifying key knowledge sectors and quantifying performance through indicator development, knowledge governance has been subsumed into national economic competitiveness. Is this the true purpose of knowledge? The use of numerical data and algorithms has also become embedded in various governance levels through performance assessments as tools of comparison and decision-​making. In turn, this usage institutionalized preconceived models, institutional practices, how we talk about the purpose of knowledge governance, and patterns of behaviour that limit reflexivity and agency. This knowledge alchemy has informed national and institutional policies on competitiveness, higher education and innovation. As we showed throughout the book, through the use of datasets and algorithmic formulas, knowledge alchemy enabled the increase in value of various entities such as cities as innovation hubs, countries as destinations of talented individuals, 144

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or higher education institutions as sources of academic excellence. The first part of the book dissected these developments in detail, starting with the field development of indicators. In order to identify the taken-​for-​g ranted presuppositions of data-​driven knowledge governance, we need to understand how indicators and rankings have entered these developments. Indicators and rankings are an output of algorithmic reasoning; indeed, the aggregate figures that produce rank orders are based on statistical operations according to predetermined logical order. Initially, the rankings dealt with good governance and the competitiveness of countries, but since the 2000s the global rankings on higher education and innovation have emerged and the results have been regularly produced to inform contemporary performance assessment. Lately, the assessment of academic research and education has become even more central in global innovation and city rankings. These have been recently followed by indicators of digitalization and AI. The effects of these rankings have been numerous: innovation, higher education and academic life more generally have been increasingly governed by high pace data-​driven reforms. The indicators have also contributed to perceptions about national and regional ‘models’ and learning from others. Analysing global indicators on education and innovation, we discussed the kinds of political value choices made in the production and use of data, and the ways in which quality is translated into quantity. We also explored the field development in global ranking, where new actors have been entering the field with alternative measurements. Though rapidly increasing in number, we observed a strong convergence in the composition of these measurements that overlapped ideationally, methodologically and epistemologically through the sharing of data. The existing data sources are now being used in composite indicators, most notably the innovation rankings, that incorporate pre-​existing data sources and manipulate them algorithmically to produce ‘new’ and alternative indicators. Attending to the ranking producers, we gained insights into the rationale as to why existing, instead of new, products are being repackaged in a different wrapping. Ideas and indicators do not exist ‘out there’. Actors are involved in their circulation and creation, and we thus examined the actors in transnational knowledge governance that have been behind global models, measurements and digital platforms. These actors are knowledge producers, brokers and users. While early on many actors operated in international organizations, they have now become increasingly affiliated with NGOs, companies, foundations, higher education institutions, and even research institutes. We analysed how international organizations have played a major role in launching and constructing the global imaginaries of competition. However, closer analysis of the production of global policy scripts point to different rationalities within these institutions and their organizational 145

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strategies. We showed how different actors such as the WEF, OECD, UNESCO, INSEAD, Clarivate Analytics and other major ranking producers created ‘digital capital’ (that is, data resources that enabled the manipulation, visualization and exploitation of data for organizational aims, often of economic nature) to integrate and shape the field of transnational policy making. By parsing out the epistemic practices of these different actors, we described the ideas of transnational knowledge governance they promoted and how these ideas are encased in models built from data. As the value of these ideas increased with unreflective adoptions of global models by new users (for example, countries, cities, institutional leaders, higher education managers, and more) seeking ready-​made global models and solutions for local problems, certain forms of knowledge alchemy are further institutionalized. The second part of the book turned to the ‘products’ of knowledge alchemy, the strategies, policies and practices that embed them, as well as the generated effects. From hourglass to astronomical clocks, watches and digital time, time is increasingly measured precisely. In higher education, a structuring development occurred when the digitization of time (accounting for time in terms of hours/​classes taught, of yearly workload, of evaluation/​self-​ evaluation, and more) was transmuted via digitalization into a value to indicate the ‘worth’ and ‘value’ of an academic (quality of teaching, efficiency, career development potential), comparable across different disciplines that are organized very differently and pursue distinct research questions. Via digitalization, time as used by academics is now transformed into a measure of economic utility –​now and the anticipated future. Transnational knowledge governance operates on perceptions of the future. The spatial and temporal aspects of these imaginaries call for attention: What kind of futures do they talk about, what is the assumed past, what kind of developments and changes are expected, and where do they occur? We analysed the references to the future and how past experiences have been projected to expectations and predictions of the future and how these in turn then impacted knowledge governance. Global indicators brought coherence to transnational governance by providing a global policy script for success in global economic competition. While the formula for success has been increasingly expressed in numbers (digitization), actors also referred to different imaginaries of knowledge governance, particularly the role of the so-​called ‘talents’ in enabling the competitiveness of countries, institutions and businesses. Examining the relationship between the global talent imaginary, the policies that projected this imaginary, the institutional practices that brought in the talents, and the factors that talents considered in their mobility decisions, we found the lived reality of talents to be far removed from the imaginary. 146

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Strategies and policies of knowledge alchemy In contemporary policy making, assumptions about the medieval scholar have informed policy makers, institutional leaders and academics about the ways in which mobility generate untold values for a continental region, a country, an institution and even the individual. These assumptions emerged from highly selective interpretations of academic mobility throughout time, starting from the existence of common curricula and degree (studia generalia), the introduction of licences to teach anywhere (ius ubique docendi), to travelling scholarships, the ‘brain drain’ versus ‘brain gain’ phenomenon, and global talent competition. Regions, countries and institutions have been engaged in a global competition for talent, with academic destinations appearing as winners and those witnessing the departure of their academics as losers. The factors that contributed to attracting and retaining academic talents only partly echoed those the strategies and policies emphasized. We looked at the case of Singapore and found the factors that motivated foreign academics to relocate to the city-​state were rather mundane: good professional compensation, research support, use of English, and being close to family in the region. These factors, however, were not sufficient for retaining recruited academic talents as initial generous offers lapsed over time. The rising costs of living and the general lack of work–​life balance were motivating foreign academics to consider their mobility out of Singapore. If a highly competitive and successful country in the global talent competition is unable to hold on to some of its key knowledge workers, the extent to which the global talent imaginary results in attractive policies and good practices need to be interrogated. We showed that some imaginaries have relied on the emergence of a selective historical narrative about the ‘value added’ of talents through mobility. These imaginaries have been linked to global megatrends, highlighting an intensifying global economic competition through digitalization and innovation. The ranking producers have identified automation as one of their observed megatrends, discussing it as a ‘fourth industrial revolution’ or ‘second machine age’, where digitalization and automation ultimately conditioned global competitiveness, innovation and knowledge governance. The coming together of these imaginaries and narratives forcefully shaped and steered competitiveness strategies, policies and practices across multiple levels, starting from the individual and moving to the organizational, institutional, national, regional and even international. The pervasiveness of these strategies, policies and practices can be found in multiple initiatives around the world. The logic of numerical governance has reached new levels in innovation and educational policies. To foster innovation, governments have turned their 147

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gaze to embedding the identified imaginaries and narratives into strategies, policies and practices. We saw this emerging in strategies targeting jobs and growth, research and higher education, AI, university alliance-​building, and the creation of innovation hubs. Innovation and knowledge creation are also tightly linked to cities as innovation hubs and to a perceived global trend of urbanization. The storyline put forward by expert organizations such as the WEF has been vague, but interestingly there have been keen references to a historical past that provided analogies to desired futures. In the past decades, global university rankings were used to measure the research performance of universities. To complement university rankings, innovation indicators on global and city level as well as subnational competitiveness rankings emerged. The global narrative of urbanization runs along the current policy concern on automation, where cities and regions as hotbeds for innovation are highlighted over the nation-​states where they are located. States appeared to take a backseat in these processes as strategies and policies are ‘uploaded’ to regional and international organizations, ‘downloaded’ to cities, and embraced by individuals with transnational lives. It is impressive how the policy actors engaged in the global talent competition are now found across all governance levels, with many claiming to take the lead necessary for the performance for their region, city and institutions. The global models on innovation hubs also evoked imaginaries and historical narratives of pre-​modern city-​states –​such as the Medici-​era in Florence –​where flows of ideas and individuals intersected and fostered competitiveness. In Asia, we find China’s Belt and Road Initiative invoking the glory of the storied Silk Road through connectivity. While the university and city rankings evaluate the creative potential of academic researchers, they provided broader assessments of the ‘innovation environment’, including the liveability and culture of the innovation hubs. However, the city-​level measurements on innovation are often products of the algorithmic data manipulation of national-​level data that are increasingly used to steer urban governance. While the figures are intended as tools of evaluation, they also have constitutive effects, imposing preconceived models of innovation. We identified an unreflective privileging of global models instead of considering local solutions and specificities, references to global rankings as social facts, or uses of global platforms and databases (Google Scholar, citation databases, registries on patents, and so on) in decision-​ making. These practices have been observed on different levels of governance, trickling down to the institutional, and imposed at the individual level when regular assessments of their value are made. To summarize, knowledge alchemy is now prevalent around the world, informing national and institutional policies and practices on global 148

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competitiveness, higher education and innovation. It is embedded in transnational administration and global policy making through numerical tools, imaginaries and narratives used across multiple policy domains and sectors. To further understand conventional power in contemporary national and transnational governance, this book has uncovered the mechanism that keeps up and reinforces a generic process of knowledge alchemy, as well as its limitations. The critical examination of global models and scripts and their changing nature also allows critical reflection over them, marking an opening for agency. We see society and the world policy as engaged in a series of struggles to assess the value or worth of institutions and individuals. We used the medieval-​era notion of alchemy to capture what we see as the taken-​for-​ granted nature of historical narratives, concepts and metaphors that are used to explain the current challenges of knowledge governance. More importantly, we see knowledge alchemy at work concerning the future of knowledge governance. The limitations of indicator-​based assessments and anticipations of future are rarely acknowledged. On the contrary, indicator-​ based processes have largely become embraced as scientific, facilitating comparisons between different objects and subjects, but, in reality, there are many particularistic, qualitative choices and valuations underlying global indicators and related policy scripts. Their acknowledgement is significant because it has major implications for globally diffused policies designed to mitigate and transform our collective futures.

Disruptions and digital train tracks Imaginaries, such as the one concerning global talent competition, can be seen as forms of ideational interests that exist in specific relationships with material interests. As discussed in Chapter 1, Max Weber saw ideational and material interests in constant interaction with one another (Weber 1959). According to his famous train track metaphor, the material interests determine the direction and speed of the train, but in some historical circumstances ideational interests can redirect the train and redefine social development. Similarly, imaginaries are not only linked to material and ideational interests but also to habitual patterns of behaviour, in the ways in how actors classify the world and behave according to cultural conventions (Duby 1980). Here we are increasingly travelling on digital train tracks, where our direction –​and future destination –​is set by the data and metrics of the past shared by the indicator producers. These digital train tracks seem rather resistant to disruptions and alternative worldviews. Indeed, the speed at which this digital train travels is lightening, as our historical past segues into our current understanding of the world and how we should behave in the future. 149

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Ideational schemas, however, usually change after an external shock or critical juncture (Marcussen 2000; Blyth 2002; Schmidt 2002). The outbreak of the COVID-​19 pandemic in early 2020 provided a major external shock to innovation and higher education. In particular, the pandemic and the restrictions on mobility of individuals that was particularly present in cross-​ border mobility posed a major disruption to talent competition and countries’ quest to attract talent. Their ability to foster and retain the skilled individuals appeared to be severed through the pandemic, and points to their overall readiness to engage in the global talent competition. The major policy brokers have reacted to COVID-​19 by identifying its interlinkages to the global talent competition in the contexts of migration, innovation and higher education. For example, the OECD sees the pandemic as an opportunity to steer the science, technology and innovation policy to address global challenges (Ramalingam 2020; OECD 2021). Yet, there seems to be little if any reconsideration of the global talent competition paradigm itself. For example, in the context of migration, the OECD approached COVID-​19 as a communication issue that requires firm action from civil service in accommodating the restrictions and the necessity of migration (OECD 2020b). At the same time, the WEF sees the pandemic as having accelerated the fourth industrial revolution for which the WEF’s GCI is a compass (World Economic Forum 2018, 1; World Economic Forum and Deloitte 2020). The 2021 edition of the GTCI and report acknowledges that ‘[a]‌lthough available data and indicators still come short of fully describing the ways in which the pandemic has changed the local and global talent scenes, it is clear that many of the changes we have seen will have a deep and lasting impact on labour markets and talent strategies’ (INSEAD and Portulans Institute 2021, v). Yet, the GTCI ranking is portrayed as a reliable companion for governments that are trying to navigate the crises. Remarkably, New Zealand ended its restrictive COVID-​19 measures and travel ban by referring to the need to allow international talent to enter the country (Fernyhough 2022). As COVID-​19 gradually fades from the front page of Inside Higher Ed, it is important to acknowledge that we are very much still living in the aftermath of the pandemic. Some effects are an increased awareness of public health and the need to coordinate public policies at national and transnational levels. With the pandemic, the key role of public authorities in countering the pandemic was also clear. There is a demand for more data on the effects of the pandemic on global higher education. This will create further business opportunities for knowledge alchemists in areas such as hybrid learning environments and distance learning, poised to increase soon. In these considerations, what place for staff and faculty well-​being? The acceleration of academic time during pandemic or disruptions are rarely acknowledged 150

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in future national or institutional planning beyond that everyone involved in higher education needs to be ready and remain resilient. For example, the idea of a modern city has become so pervasive that there is little room for alternative future imaginaries of a post-​carbon society (Hajer and Versteeg 2019). This can also be seen behind the global talent competition paradigm that takes the contemporary ways of urban life and intensive global mobility of individuals as given without considering its possible limits, most notably through the climate change. And though talent competition is seen as a remedy for the uncertainties caused by AI or ‘the fourth industrial revolution’, the rise of automation also questions the very premises of human capital, marking even the potential limits of knowledge economy and growth theory (O’Donovan 2020). In his pioneering work, Histoire du climat depuis l’an mil (Le Roy Ladurie 1967), French historian Emmanuel Le Roy Ladurie demonstrated that the climate was not a global constant, but varied a great deal secularly, yearly and monthly. Today, this discovery goes hand in hand with a new paradigm. After having been for millennia the object of climate change, today man significantly impacts the biosphere. Le Roy Ladurie’s work focused on the growth of trees, the dates of the wine harvest and the lives of glaciers. Methodological questions included the effects of a hard winter on agricultural yield, the impact of spring after winter, and the effects caused by changes in temperature. For Le Roy Ladurie, the overarching research issue was the correlation between secular fluctuations and the history of man. If Le Roy Ladurie was a forerunner of historical research on the climate, another French scholar, sociologist and philosopher, Bruno Latour, focused on the latter part of his career on the Anthropocene and the relationships between man and the climate. While Le Roy Ladurie kept a scholarly distance to the climate, Latour was concerned about the impact of human agency on climate change (Latour 2014). In his mind, this impact meant that the distance between man and the environment was gone, as was gone the objectivity that went with it. What needed to be re-​evaluated was man as ‘the active subject of history’ (Latour 2014, 2). Humans are now in the centre of climate change, and the traces of their action are everywhere. The question is how we can consider human agency not only in the construction of facts but also in the ‘very existence of the phenomena those facts are trying to document’ (Latour 2014, 2). We have been inspired by philosopher Michel Serres’ prophetic book on the natural contract (Serres 1990) in which he showed how nature had become a major historical actor. For Serres, because of the uncontrolled violence that humans perpetrate upon the earth, a contract between the Earth and its inhabitants equivalent to Jean-​Jacques Rousseau’s social contract had become necessary. Like the social contract brought order to society the 151

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natural contract would, by attributing legal personhood to nature (Serres 2018), balance our relations with the planet that gives us life. What Serres and Latour call for is a paradigmatic change in our approach to globality, and to the natural and social environment in which we live. The Earth should not be treated any more like any object. While we have the tools to view the globe, they are currently used to further particularistic interests and values. This is also visible in higher education and research, where undemocratic political and economic drivers such as the WEF and individual knowledge alchemists are the Earthmakers, to use Clive Hamilton’s coinage. In his text ‘Agency at the Time of Anthropocene’, Bruno Latour tries to outline the role of human agency in the climate crises. He also draws attention to storytelling as an act of ‘being in the world’: Storytelling is not just a property of human language, but one of the many consequences of being thrown in a world that is, by itself, fully articulated and active. It is easy to see why it will be utterly impossible to tell our common geostory without all of us –​novelists, generals, engineers, scientists, politicians, activists, and citizens –​getting closer and closer within such a common trading zone. … The reason why such a point is always lost is because of a long history during which the ‘scientific worldview’ has reversed this order, inventing the idea of a ‘material world’ in which the agency of all the entities making up the world has been made to vanish. A zombie atmosphere, in which the official version of the ‘natural world’ has shrunk all the agents that the scientific and engineering professions keep multiplying, comes from such a reversion: nothing happens any more since the agent is supposed to be ‘simply caused’ by its predecessor. All the action has been put in the antecedent. The consequent could just as well not be there at all. As we say in French: ‘il n’est là que pour faire de la figuration’ (it is only there to make up the numbers). … The great paradox of the ‘scientific world-​view’ is to have succeeded in withdrawing historicity from the world. And with it, of course, the inner narrativity that is part and parcel of being in the world. (Latour 2014, 13) What we ultimately need is to acknowledge not only pieces of evidence that stand in contradiction to our belief system but also different stories, narratives that challenge the ones that we have seen as foundations of our worldviews and policies. Hence, the agency of global knowledge governance lies in the social act of storytelling, not only recounting stories told by others but imagining new ones and sharing them. Disruptions such as pandemics and wars indicate that we are far more interdependent than we would regularly acknowledge. Indeed, this is 152

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often overlooked when narratives such as the global talent competition are amplified in policy circles, implemented in institutional settings, and are seen to structure individual thinking. Here, competition is emphasized even though collaboration has been very much part of this story, as we have shown in the case studies of the Europe of Knowledge and China’s university alliance-​building efforts through the Belt and Road Initiative. Indeed, collaboration can be conscious and planned, but it is generally implicit when different entities legitimize one another indirectly and, instead, focus on articulating their performance in competitive terms. In his work, historian Jeremy Adelman (2021) tells us how ‘strangers affect each other’s lives, even if they don’t realize it and they never know one another or meet one another’. This ‘need for strangers’ reminds us that the world is very much enclosed and the need to think about collaboration is equally important. But what would a narrative about global collaboration for talent look like? Given the centrality of competition thinking in the global imaginary whereby individuals, institutions, countries and even regions are pitted against their counterparts, would collaborative efforts have equal weight as those that are deemed to be competitive? Or would collaborative and competitive efforts be compartmentalized and considered independently, albeit the bias towards competition is always there to tilt the needle in its favour? There is no easy answer. Mapping how ‘collaboration’ as a term has been used in political activities, Digeser (2022: 200–​201) argues that it is highly ambiguous: it can be used in a positive light such as a ‘valued partnership’, or negatively to condemn particular compromising individuals, institutions and acts, or even in a neutral way (‘working with others’). The different depictions of Vichy France that Digeser offers are examples of how we need to collectively move away from assumptions of uncontested and singular meaning of terms. The lesson that Digeser provides concerns the danger of automation. Automation stemming from digitalization and quantification of indicators override nuances and the need to consider subtleties and complexities. The arguments that we presented in this book point to this danger as knowledge alchemists tinker with different compositions of the same elements to bring forth ‘new’ knowledge.

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188

Index References to figures appear in italic type; those in bold type refer to tables. References to footnotes show both the page number and the note number (25n1). A Academic Conferences  134, 135 academic diaspora  117–​118 academic immobility  106, 107 as a negative experience  105, 107 as a positive experience  106, 107, 118 academic mobility  94–​95, 119, 125, 147 categories of  107 and China  106 family formation in  106–​107 and Great Wars  101–​103 and income generation  99 and Italy  106 and Japan  106 as a lived experience  104–​107 in medieval Europe  96, 97–​99 in modern empires age  99–​101 as a negative experience  105–​106 presuppositions concerning  121 salaried lectureship  98 academic pilgrimages  97, 98 Academic Ranking of World Universities (ARWU)  26, 30, 34, 39, 44, 137–​138 accountability  4, 27, 28, 38, 124, 125 Ackers, L.  106 actionable indicators  22, 25–​26 actors  3, 5, 14–​16, 22, 25–​26, 48–​50, 64–​65, 68–​72, 77, 79, 84, 87–​88, 91–​92, 112–​ 117, 119, 135, 142, 145–​146, 148–​149 Adecco Group  65, 68 Adelman, J.  153 Affordability and Accessibility Comparison of Global Higher Education Rankings  31 agency  8, 80, 86–​88, 91–​93, 143–​144, 149, 151–​152 aggregation  27–​28 agricultural revolution  82 alchemy  1–​3 algorithmic governance  4, 81, 84, 143–​144

Alliance of Belt and Road Business Schools  130 Alliance of Belt and Road Environmental Deans  130 Alliance Sorbonne University  65 alternative future  88, 91, 151 Amsterdam  137, 139–​140 anticipatory governance  14, 90–​93 anticipatory innovation governance (AIG)  64, 78, 88–​90 Apple  10 artificial intelligence (AI)  4, 46–​47, 54, 61, 70, 78, 84, 88, 91, 126, 151 applications  128 automation through  78, 81–​82 EU regulations  4, 127–​128 global governance of  87 indicators of  62, 63 readiness  64 uncertainty  80 Arts and Sports Events  134 Asia Deep Dive Programme  134 Asian Universities Alliance  130–​135, 142 Asian Universities Alliance Scholars Award Programme  134 Asian Universities Alliance Staff Exchange Program  134 Asian Universities Alliance Youth Forum  134 Assessment of Higher Education Learning Outcomes (AHELO)  36, 37 assumed identities  81 A.T. Kearney  50, 51, 53 Australia  35, 113 automation  5, 10, 77–​78, 88, 143–​144, 147 and algorithmic governance  80 danger of  153 and imaginaries of competitiveness  79 through AI  see artificial intelligence (AI) World Bank on  83 see also artificial intelligence (AI)

189

KNOWLEDGE ALCHEMY

B Barben, D.  89 Bauder, H.  105–​106 Becker, G.  62 Belt and Road Initiative, knowledge governance via  129–​135, 148, 153 Belt and Road Initiative University Alliance  130, 131 Bismarck, Otto von  82 Bologna, University of  97 Bologna Process  99, 122, 124 brain drain  97, 103, 147 brain gain  103, 147 Bratt, M.  72 Brazil  68 Breton, T.  71 BRICS countries  50 British QS Intelligence Unit  66 C Cambridge, University of  42, 102 Canada  30, 67, 114 Cappelli, P.  72 cash for prestige  39 Centre National de la Recherche Scientifique (CNRS)  43 Cerna, L.  5 China  7, 68 and academic mobility  106 discipline plans  34 the field development of global ranking  30 five-​year plans  30 higher education institutions  30, 34 higher education policy planning  34 ranking in voice and accountability  28 see also Belt and Road Initiative, knowledge governance via Chou, M.-​H.  5, 101 cities  84, 148 and competitiveness  46, 54 hub construction  135–​141 as innovation economies  53 as innovation hubs  78 rankings of  21, 49–​54 smart  86, 139 City Change Plan  54 Clarivate Analytics  34, 146 climate change  82, 89, 135, 151 Cobban, A.B.  97–​98 Code of Conduct for the Recruitment of Researchers  124 collaboration  49, 61, 90, 129, 130–​131, 134, 136, 153 Columbia University  66 commodification  10, 128, 144 common curricula and degree (studia generalia)  97, 98, 147 Compass  50

competition/​competitiveness  5, 54, 93, 104, 149 assessments, city and regional level  51 and cities  46, 54 as countries’ or cities’ ability  84 digitalization  62, 77 economic competitiveness  1, 5, 12, 14, 36, 49, 124, 135 and Europe  126–​129 global indicators  23 global models and metrics of  5 imaginaries of  79–​80 narrative  95–​96 national  143 and rankings  78–​81 Competitiveness of Cities report  50 Conference Board  66 conventional power  8–​12 conventional power 2.0  11 conventions  87 Copenhagen  99, 139 Copenhagen University  99 Cornell University  65 corruption  22, 27, 67 COVID-​19 pandemic  90, 118, 141, 150 Crunchbase  50 cultural institutions  6 culture  13, 15, 16, 148 D Dassault Systèmes  70 data sources network  55 Davos Circle  70 de Meyer, Arnoud  72 de Ridder-​Symoens, H.  98 Le Défi Américain (The American Challenge)  103 Dehaze, A.  65 democratic participation  92 Digeser, P. E.  153 digital capital  34, 146 digitalization  5, 10, 77, 146–​147, 153 and competitiveness  62, 77 and ranking  4–​6 digitization  1, 5, 7, 10, 73, 77, 78, 82, 126, 146 and imaginaries of competitiveness  79 uncertainty  80 World Bank on  83 disruptions  78, 83–​84, 87, 93, 149–​153 Double First Class University  34 Dutta, S.  65, 70 E Ecole des hautes études commerciales  69, 72 École normale supérieure Cachan  43 École Polytechnique  43 economic competitiveness  1, 5, 12, 14, 36, 49, 124, 135

190

INDEX

Economist Intelligence Unit  50 ‘Emigration of Scientists from the United Kingdom’  102 Enterprise Singapore Strategy  141 Erasmus Programme  122 Erkkilä, T.  39 Europe  migration policy sector  123 technological sovereignty and global competition  126–​129 universities, comparison between  124 values of  128 Europe of Knowledge  121–​126, 153 European Charter for Researchers  124–​125 European Commission  49, 71–​72, 121, 123, 126–​128 on AI applications  128 alternative university ranking proposal  35–​36 Joint Research Centre (JRC)  67 on technological sovereignty  126 European Higher Education Area  121, 122 European higher education institutions  39 European Innovation Scoreboard  50 European Research Area  121, 122–​124 European Union  36 action scheme for university student mobility  122 AI regulation  127 and AI regulation, impact of  127 Charter of Fundamental Rights  87 competitiveness  126 higher education strategy  38 Innovation Union Scoreboard  50 Lisbon Strategy  50 multi-​annual research funding schemes  122–​123 policies on the ‘modernization’ of higher education  125 Regional Competitiveness Index  50, 51, 53 regulating privacy  127 talent competition imaginary  123 European Universities Initiative  45 Evans, P.A.L.  69 Exhibition Scholarship (1851)  101 experience and expectation  79 F Feng, Z.  130 Fifth Global Survey of International Association of Universities  38 Finland  38 41st seat of the Académie française  3, 9 Foucauldian governmentality  26 fourth industrial revolution  5, 61–​62, 77, 82, 147, 150–​151 France  35, 38, 39, 41 France Télécom  71 Fraser Institute  66

Frazer, Sir J.  9 French Alternative Energies and Atomic Energy Commission  43 Freud, S.  8–​9 future  alternative  88, 91, 151 ideal  92 politics of  90–​93 power to shape  91 symbolic  92 G Gao, L.  130 Giddens, A.  14, 26 Global AI Index (GAI)  62, 63, 64 Global Cities AI Readiness Index (GCAIRI)  62, 64 Global Cities Outlook  53 Global City Index  51, 53 Global City Talent Competitiveness Index 65 Global Competitiveness Index (GCI)  46, 47, 50, 54, 55, 57, 80, 150 indicators of innovation in  60 indicators of mobility in  59 Global Information Technology Index  65 Global Innovation Index (GII)  46, 47, 54, 55, 57, 65, 80 indicators of innovation in  60 indicators of mobility in  59 Global Integrity Index  29 global policy scripts  see policy scripts Global Power City Index  53 Global Power City Index (GPCI)  46, 47, 54, 55, 57 indicators of innovation in  60 indicators of mobility in  59 global rankings  1, 7, 67, 136 Academic Ranking of World Universities (ARWU)  26, 30, 34, 39, 44, 137–​138 Affordability and Accessibility Comparison of Global Higher Education Rankings  31 of competitiveness  78–​81 composite indicators  13 field development of  22–​26, 25, 30, 46, 145 QS World University Rankings  32–​33, 34 SCImago Institutions Ranking  32–​33, 35 Shanghai Ranking  26, 31, 35, 38, 41, 92, 124 Webometrics Ranking of World Universities  31, 35 see also higher education; rankings global talent competition  6, 46, 61, 64, 73, 95–​96, 150–​151, 153 human capital, mobility and innovation  56–​61 imaginaries  47, 123 open society and urbanity  84–​86 overview  54–​56 script  47, 77–​78

191

KNOWLEDGE ALCHEMY

see also academic mobility; human capital; talent Global Talent Competitiveness Index (GTCI) 46, 47, 53, 54, 55, 57, 64–​71, 68, 80, 150 advisory board  71–​72 indicators of innovation in  60 indicators of mobility in  59 globalization  14, 27, 42, 82, 84, 120 glocalization  8 Godin, B.  48 Google  64, 68, 70–​71, 81 Brain and Translate  70 and GTCI process  70 Scholar  35 Goulard, S.  71 Government AI Readiness Index (AIRI)  62, 63 Government at a Glance  29 Gravier ruling  121 gross domestic product (GDP)  62 growth theory  88, 151 Guston, D.H.  89

Hong Kong  7, 140–​141 Hot Spots 2025  50 Houssaye, A.  3, 9 human capital  5–​6, 56, 57, 82–​84, 140, 141, 144, 151 composition of  63 indicators  22, 23–​24, 61, 61–​62 indices  64–​65, 80–​81 well-​being and AI indicators  63 see also global talent competition; talent Human Capital Index (HCI)  62, 80, 83–​84 Human Capital Leadership Institute (HCLI)  65, 68 Human Resources Strategy for Researchers  124 Humboldtian tradition  124 Hungary  68

H habituation  15 Hamilton, C.  152 Harvard University  102 Havel, V.  128 Hazelkorn, E.  38 Henry, A.  72 Heritage Foundation’s Index of Economic Freedom  53 higher education  6, 149 in China  30, 34 commodification in  10 EU strategies and policies  38, 39, 121, 122, 125 in France  38, 41 global indicators  23 and national economic competitiveness  49 non-​aggregated indicators of  25, 36, 37 ranking tools and the numerical goals  10 rankings  31, 36, 37 in Singapore  see Singapore and skills development  84 structuring development, and digitization of time  146 symbolic domination in  11 Times Higher Education (THE)  30, 32–​33, 34–​35, 92, 124 Times Higher Education Supplement (THES)  31, 34 in UK  30, 38, 99–​100, 102 see also rankings; university rankings historical narratives  79, 83, 85–​86, 88, 93, 147 on city-​states  85, 148 and imaginaries  80 of industrialization  82 and policy scripts  79 Hollande, F.  43

I ideational models  85 ideational schemas  14, 15, 150 imaginaries  14–​16, 80–​81 of competition/​competitiveness  78–​81, 87, 93 and globalization and global governance 14 importance for capitalism  14 of knowledge governance  77, 146 and limits of policy scripts  87–​88 narratives and discourses of competitiveness  80 socio-​technical  14 talent competition imaginary  123 immigrations  104 India  68 industrial revolution  82 Initiative d’excellence (IDEX)  43 innovation  46, 54, 61, 140–​141, 149 anticipatory innovation governance (AIG)  64, 78, 88–​90 and cities  53, 78 European Innovation Scoreboard  50 and global talent competition  56–​61 governance  12, 47 hubs  49, 135, 137–​138, 148 indicators of  60 and national economic competitiveness  5 national innovation system  48–​49 OECD’s Anticipatory Innovation Governance initiative  64, 78, 88–​90 rankings  46, 52 and science  48 technological  48 time and place of  48–​49 Innovation Cities Index  50, 54 innovation ecosystems thinking  87 Institut Européen d’Administration des Affaires (INSEAD)  12, 56, 65–​66, 68, 72, 81, 146

192

INDEX

CTCI  see Global Talent Competitiveness Index (GTCI) position in Financial Times global rankings  67 institutional analysis  13, 15 International Alliance of the Belt & Road Engineering Education  130 international governmental organizations  27 International Institute for Management Development  72 International Labour Organization  66 International Telecommunication Union  66 internationalization  38, 105, 122 Ishikura, Y.  72 Italy, and academic mobility  106

Locke, W.  38 Lovell, B.  103 M Macron, E.  41, 45, 71, 126, 129 magisterial universities  97 mappings  25, 29 Marx, K.  14 MasterCard  50 Merton, R.K.  3 Montaigne, M. de  91 Moody’s credit rating  53 Mori Memorial Foundation  50, 53

J Japan, and academic mobility  106 Joint Research Center  72 Joint Research Programmes  134, 135 Jöns, H.  102 K Karlsson, M.  71 King Abdulaziz University (KAU)  39 Knack, S.  25n1 knowledge  alchemy  1–​4, 8–​12, 143–​149 -​based economy  49 devaluation  11–​12 diplomacy  7 economy  88, 151 local  8 political economy of  144 producers  12–​13 production  5, 12 knowledge governance  1–​2, 4, 143 models and agency in  144–​146 and national economic competitiveness  144 and non-​state actors  50 producers of  64–​65 transnational  146 Koselleck, R.  15, 48, 79 L Lanvin, B.  65, 69, 72 Lanzhou University  131 Latour, B.  151–​152 Latvia  68 Lausanne  72 Le Roy Ladurie, E.  151 league table formats  27 Legatum Institute’s Legatum Prosperity Index  66 Leiden Ranking  32 Li, S.  34 licences to teach anywhere (ius ubique docendi)  98, 147 Lisbon Strategy  123, 125

N Nanyang Technological University (NTU)  108, 112 narratives  16, 80 National Science Foundation  103 National University of Singapore (NUS)  108, 112 natural contract  151–​152 Netherlands, the  35, 67, 139 Network Readiness Index  65 Neveu, E.  41 new actors  25–​26, 36, 145 New Silk Road  130 New Zealand  150 Newton, Sir I.  2 Nicolas S.  42 Nokia  10 North American Educational Policy Institute  35 numbers  47, 77 data  144 numerical governance  16, 83 as quantitative classifications  2–​3 NUTS 2  53 O objectification  47, 54, 78–​79, 143 OECD  28, 36, 48, 66, 72, 146 Anticipatory Innovation Governance initiative  64, 78, 88–​90 Better Life Index (BLI)  62 Program for International Student Assessment (PISA)  56 view on COVID-​19 pandemic  150 Oliver Wyman Forum  62 Open Budget Index  29 Open Method of Coordination  123–​124 Open Net Initiative  29 open society  81, 85 openness  66, 85–​86, 91, 129, 136 Overseas Study Programme  134 Oxford Insights  62 P Palonen, K.  25

193

KNOWLEDGE ALCHEMY

Panthéon-​Sorbonne  69 Paris, University of  97 Paris-​Saclay University  21, 39, 41–​44, 45 Pécresse, V.  42 peer pressure  11, 29 Perraton, H.  97, 99–​100 Pietsch, T.  101 Piironen, O.  39 Pinchai, S.  70 platform economy  86 Poland  68 policy diffusion and convergence  6–​8 policy narratives  13, 15 policy scripts  6, 8, 12, 13, 37, 44, 47, 56, 77, 79, 93 politicization  25, 28, 88 Portulans Institute  69–​70 pre-​modern city-​state  148 presuppositions  95, 145 productivist biases  16 Q QS World University Rankings  32–​33, 34 Quacquarelli Symonds  34 quantification  143–​144, 153 quiet power  7 R rankings  7, 12, 145 and China  28, 30 of cities  21, 49–​54 and competition/​competitiveness  78–​81 of competitiveness  22 of countries and innovation hubs  137–​138 criticism on  27–​28 and digitalization  4–​6 of economic competitiveness and innovation  78 of good governance  22, 26–​29 of innovation  46, 52 Leiden Ranking  32 and quality and quantity  9 references to  40 regulation and role of government  86–​87 successful strategy  39 as tools of symbolic power  37 of universities  4, 29–​37, 49, 124 of US colleges  29 usefulness of  38 world-​class university  39 see also global rankings rational choice theory  8 rationality  7, 9 rationalization  3, 6, 13 reactive modality  91 readiness modality  91 reflexivity  12, 15, 17, 41, 47, 92, 139, 144 regional universities  97 Retailleau, S.  41–​42

revolutions  81–​84 Reynoso, R.E.  72 Rhodes Scholarship  100–​101 Rousseau, J.-​J.  82 Russia  68 S Sarkozy, N.  43–​44 Scientific Visa  124–​125 SCImago Institutions Ranking  32–​33, 35 Scott, P.  104 second machine age  77, 82, 147 second-​generation indicators  22, 25, 28–​29 characteristic criteria  25n1 of good governance  36 self-​defeating prophecy  92 self-​fulfilling prophecy  92 Serres, M.  151, 152 Servan-​Schreiber, J.-​J.  103 Shanghai Jiao Tong University’s Institute of Higher Education  30 Shanghai Ranking  26, 31, 35, 38, 41, 92, 124 Singapore  7, 67, 96, 119, 140–​141, 147 as an attractive academic destination  112–​115 attracting and retaining foreign academic talents  107–​112 and Australis, difference between  113 and China, linguistic environment between  114 cost of living in  115, 116, 118 easier access to research funding  113–​114, 117 educational subsidies  118 ‘English-​knowing bilingualism’ policy  114 and ‘global’ flows of academics  117–​119 housing issues  116, 118 moving away from  115–​117 and moving closer to parents  112, 114–​115, 117 multilingual environment in  114 Overseas Networks & Expertise Pass (ONE Pass)  141 remuneration package  113, 117 Research Innovation Enterprise Plan 2020  140–​141 and US, difference between  113 use of English language in  117 work–​life balance, lack of  115, 116–​117, 118 Singapore Management University  72, 112 Single Market  123 skills  84, 127–​128 Slovakia  68 Smith, A.  82 Sodexo  70 soft connectivity  85–​86 soft power  6–​7, 100 Sorbonne Declaration  122 Sorbonne University  41 Startup Ecosystem Report  50

194

INDEX

Stockholm strategy  139 Stockholm’s Vision 2040  136 storylines  13, 16, 47, 77, 79 storytelling  152 structuration  12, 13–​14, 25 Student Visa  124–​125 student-​controlled universities  97–​98, 99 subjectification  79, 85–​86 Surdez, M.  41 Switzerland  67

University Consortium of the 21st Century Maritime Silk Road  130 university rankings  31, 36, 37, 49–​50, 56 administrators and politicians’ views  37 European Commission alternative ranking proposal  35–​36 ranking tools and the numerical goals  10 see also global rankings; higher education; rankings Uppsala University  99 urbanization  78, 85, 148

T Taiwan  35 talent  78, 79, 81, 84–​86, 104, 146 definition of  5 international knowledge workers  139 mobility  5, 54 see also academic mobility; global talent competition TATA Communications  65, 68 Temasek Holdings (Private) Limited  65 Temasek Management Services  65 Thomson  71 Thomson Reuters  34, 56 time  13–​15 Times Higher Education (THE)  30, 32–​33, 34–​35, 92, 124 Times Higher Education Supplement (THES)  31, 34 Toffler, A.  88 Tõnurist, P.  90 Tortoise media company  62 2thinknow  53, 54 train track metaphors  14, 149 Transparency International’s Corruption Perception Index  22 travelling scholarships  100–​101, 147 see also academic mobility Tsinghua University  65, 131

V valorization  140 value creation  10 from nothing, conditions for  10–​11 and social carriers  11 Velvet Revolution  128 Versteger, M.  71 von der Leyen, Ursula  126, 128

U UK universities/​higher education institutions  30, 38, 99–​100, 102 U-​Multirank  36 United Arab Emirates  135 United Nations  72 Conference on Trade and Development (UNCTAD)  66 Declaration of Human Rights  87 ICT Task Force  69 UNESCO  66, 146 United States  50, 67 -​based institutions  30 proportion of immigrants in  108 universities  35, 102–​103 University Administration Meetings  134 University Alliance of Belt and Road Deans  130 University Alliance of the Silk Road 130–​135, 142

W Walker, K.  70 Weber, M.  3, 11, 14, 149 Webometrics Ranking of World Universities  31, 35 well-​being  4, 61, 61–​62, 63 Wharton School of the University of Pennsylvania  65 World Bank  5, 87 Country Policy and Institutional Assessment tool  27 on digitization and automation  83 Human Capital Index (HCI)  62, 80, 83–​84 Worldwide Governance Indicators (WGI)  27, 28, 64 Worldwide Governance Indicators and Ease of Doing Business ranking  22, 53 World Class 2.0  34 World Economic Forum (WEF)  4, 5, 7, 50, 54, 56, 66, 72, 78, 81–​82, 85, 146, 148, 150, 152 GCI  see Global Competitiveness Index (GCI) on geopolitics and global governance  87 Global Competitiveness Report  64 Global Information Technology Report  65 and regulation  87 World Intellectual Property Organization (WIPO)  65, 66 World Society  6 world-​class universities  34, 37–​40 Worldwide Governance Indicators  22 X Xi Jinping  34, 129 Xi’an Jiaotong University  131, 133–​134 Y Yale University  66

195