1 Introduction

Over more than half a century, the dominance of International Organisations (IOs) in the production of global metrics has transformed global governance. However, amidst the avid critics and unapologetic fans of ‘governing by numbers’, it is still surprising that we know so little about the ways in which global processes of quantification are reconfiguring IOs' work in the fast-moving field of global challenges. Metrics have infiltrated not only organisational cultures and the environments these organisations inhabit; crucially, they are reshaping the ways IOs co-exist, compete and survive in an increasingly datafied, yet uncertain world. Recent decades have seen fervent activity to build working collaborations and broad alliances for producing expert knowledge on global challenges. Financial investment in IOs’ collaborations is increasing and so is hope, as more and more policy actors place emphasis on global synergies and partnerships. Given the moral dimension that these measurements of progress have taken, as well as the enormous human and environmental cost of their failures, The New Production of Expert Knowledge: Education, Quantification and Utopia casts light on the role of International Organisations in producing expert knowledge for transnational education governance.Footnote 1

Building on the findings of the European Research Council METRO research project (2017–2022), the book offers an interdisciplinary analysis of the interrelationships of International Organisations (IOs) in constructing the global metrological field. Education is the focal case for this examination: despite the prevalent idea that education is a predominantly national matter, IOs have been central to processes of standardisation, de-contextualisation and the performance management of education through numbers. As a result, they have been instrumental in commensurating, and therefore transforming the field. In addition, education governance has been attracting larger policy significance, as it is increasingly considered central to both economic prosperity and social cohesion. Education is a key element in the newly emergent well-being and ‘better life’ strategies that have prevailed in the statistical governing project post the 2009 financial crisis (Stiglitz et al., 2009); more recently, after the global pandemic of 2020–2023, it is again education and its ‘losses’ that are counted and planned for. Last but not least, education is a key policy arena in monitoring projects, given first, its history in the establishment of testing and the associated calculative technologies that came with it, as well as its congruence with efforts to use ‘softer’ data sets for calculating the social. These are just some of the many reasons that large IOs like UNESCO, the OECD, the European Commission and the World Bank have invested large amounts of data and expertise in education from the mid-twentieth century on. The encoding of education data processes and organisational cultures that these commensurating practices require (in order for data to be shared and co-produced) represents a microcosm of the workings of quantification for transnational governance. In other words, the examination of the production of new governing knowledge in education by IOs is a unique opportunity to open, rather than stack yet another ‘black box’ in the field of global governance (Bhuta, 2012).

However, the book goes beyond an examination of the role of IOs in ‘governing by numbers’. As previously suggested, multiple bodies of knowledge are brought together in order to analyse the role metrics play in reshaping the relationships between the data collectors themselves. Thus, a core objective of the analysis is to understand the role of quantification in the interplay of IOs. Second, although there have been some in-depth studies of the impact of measurement on reforms in various policy fields, little attention has been paid at those early, yet crucial, venues, actors and activities that determine processes of problematisation (the construction of the ‘problem’) and institutionalisation (the moment the ‘problem’ enters institutional agendas). Third, and most important, this book’s starting point is that numbers and (international) organisations have come to be mutually constitutive. Numbers move: this seemingly simple, yet unique quality has created a fluidity between internal organisational arrangements and external environments, as well as among IOs themselves. Hence, going beyond classic organisational sociology’s distinction between internal structures and external contingencies and environments, this book discusses the ways that numbers—with their qualities to simplify, stabilise and travel—reconfigure relationships, dependencies and structures of organisations and fields in fresh and politically salient ways; in other words, they come to govern them.

As previously suggested, the book analyses in-depth empirical findings, as they emerged from the European Research Council funded project ‘International Organisations and the Rise of a Global Metrological Field’ (METRO, for short). The project run in the period between 2017 and 2022 and involved a comparative case study of different policy fields, examining the Sustainable Development Goals as a whole, but also focusing deeper on the cases of education (in particular, SDG4 and the emergence of the ‘European Education Area’), the global monitoring of poverty and the case of statistical capacity development (as it became increasingly key across all the SDGs). The study used mixed methods, combining textual analysis, social network analysis, as well as over 80 interviews with key experts in International Organisations, including the World Bank; the United Nations Educational, Scientific and Cultural Organization (UNESCO); the UN Children’s Fund (UNICEF); the UN Development Programme (UNDP); the World Health Organization (WHO); the UN Statistical Division (UNSD); and the Partnership in Statistics for Development in the 21st Century (PARIS21). In addition, the research team drew on the careful analysis of official documents produced by this epistemic community, including flagship reports, policy and strategic documents (such as declarations, position papers and action plans), internal documents produced by IOs (including meeting agendas, open consultations and PowerPoint presentations) and research articles published by actors in these networks. Empirically, the central analytical approach was inspired by grounded theory, entailing multiple rounds of coding (including descriptive, focused and theoretical coding) (Charmaz, 2006). Conceptually, the project built on and synthesised political sociology, Science and Technology Studies (STS) and theoretical strands from the field of the social studies of metrics.

2 Global Numbers and the Work of IOs

Despite the renewed prominence that is given to the need for alliance-building by IOs, collaboration has always been central to their operation, since they have traditionally needed to work closely with governments, NGOs and the private sector. Yet, the complexity of ‘wicked’ problems, ‘donor duplication’ (Ringel-Bickelmeier & Ringel, 2010), resource-pooling and data overload have become some of the most common reasons that IOs are increasingly compelled to work together. Indeed, most major global strategies, such as the Millennium Development Goals (2000–2015), the post-2015 Sustainable Development Goals or major education testing regimes, such as the OECD Programme for International Student Assessment (PISA), are collaborative endeavours, dependent on pooling of resources and expertise. How do these IOs learn from one another? In the making of numbers, how do they negotiate knowledge controversies and share the expertise they generate? How do they actively produce collective sense-making (Weick, 1995) and issue-framing strategies (Baumgartner & Jones, 1993)? How much do we know about their expert networks? How do they manage to produce expertise together, while maintaining their unique branding and contribution in the field? Ultimately, if rating and ranking practices are a ‘zero-sum’ game for the assessed, how much do we know about the rules of the game for the assessors?

Although questions still abound, at least in the field of education, the production of data and numbers has—for some time now—become a key mechanism of both knowing and governing the field. Complex statistical systems—best exemplified by the Sustainable Development Goals (SDGs) introduced in 2015—as well as performance measurement instruments, such as the OECD’s Programme for International Student Assessment (PISA), have emerged as tools for both monitoring and steering global education action. This process of hyper-quantification has had far-reaching consequences: as numbers evolve from merely instruments for governance to ‘civic epistemologies’ (Jasanoff, 2004); they reshape the broader context in which knowledge about problems is produced, and thus change political identities, relationships and institutions (Bandola-Gill et al., 2022; Miller, 2001). At the same time, quantification is as powerful as it can be paradoxical: measurement is not a neutral activity but located at the intersection of diverse (and often competing) epistemic and value orders.

More specifically, we know well by now that the power of quantification is firmly positioned in such epistemic virtues as objectivity and political neutrality (Porter, 1995). Historically, the power of numbers stemmed from their ability to represent—and construct—governing problems, underpinned by the technocratic legitimacy of the seemingly a-political statistical method (Grek, 2010). Nevertheless, one of the core arguments of this book is that the power of numbers is equally grounded in their political value—and this value is increasingly foregrounded on the global arena. Thus, the former push for depoliticizing decision-making on the basis of evidence has been more recently counterbalanced by the re-politicisation of education metrics, particularly as a result of the ‘participatory turn’ in the global monitoring systems (Bandola-Gill et al., 2022): the ‘turn’ necessitated the wider participation of actors in number-making, including from countries from the Global South (Fukuda-Parr & McNeill, 2019), with the aspiration to create opportunities for more democratised statistical systems (Milan & Treré, 2019; Tichenor, 2022). Increasingly, the production of numbers is expected to go beyond ‘global’ numbers and instead to account for ‘local’ politics and needs—or at least to give them equal weight, in that no global numbers can be produced without the active co-option of local actors and their needs.

This tension between technical and political accountability, or in other words, between authoritative and democratising numbers, is at the crux of this book: although the empirical work behind the METRO project set off from an interest in exploring the interplay between IOs, what the fieldwork manifestly showed is that the notion of interplay goes well beyond the confines of understanding how IOs collaborate in the production of metrics. Instead, the research team was confronted by experts in the field who did not see them as authoritative actors, distant from their field of enquiry in producing objective knowledge about it; on the contrary, they saw themselves as caring figures, that accepted the political power of numbers, and were largely dependent—restrained and enabled in equal measure—on the complexities of the participatory processes of producing global knowledge and governing in a post-truth world.

Before going into the detail of the main arguments that are presented in this book, I would first like to offer a brief overview of the development of expertise in transnational education governance from the late twentieth century today: what began as the first efforts to establish comparative data in education, swiftly turned into evidence-based policy and ‘what works’ in the 1990s, to develop further to the establishment of European and global soft governance tools, such as the Lisbon Agenda at the European level and the Millennium Development Goals globally. One of the core STS conceptualisations on the shift from Mode 1 to Mode 2 scientific knowledge production will be briefly discussed here, as a useful tool for explaining the many further developments and changes in the making of global education expertise. While I fully acknowledge the limitations of the Mode 1 to Mode 2 frame, I use it here as a heuristic device that allows to present both the data and the analysis more clearly. Finally, I will introduce the book’s chapters, with the aim to show the ways that post-2015 education expertise is characterised by new qualities and characteristics in the ways that it is produced, negotiated and communicated.

3 Governing Knowledge, Experts and Data in the Twenty-First Century

The last thirty years have seen a major shift in the production of education research for policy. In this section, I examine the specific case history of the emergence of this new knowledge production regime in the field of education research, starting from Europe and the United States and spreading globally. During this period, changes in policymaking, which have been summarised as a shift from government to governance, and changes in knowledge production (including increasingly algorithmic knowledge and artificial intelligence) (Gibbons et al., 1994; Nowotny et al., 2001) have come together symbiotically: changing governance processes and norms create the conditions for new kinds of knowledge production, and such production of expert knowledge for policy becomes a key resource for governing the ‘perma-crises’ that contemporary societies find themselves in, experiencing the compounding challenges of climate change, global pandemics, inequalities and more.

The idea of a ‘governance turn’, as marking a significant shift in governing practices in Europe and beyond, continues to be of relevance in the analysis of education research (Beukel, 2001; Hooghe & Marks, 2001; Mayntz, 1994). In brief, governance describes a move from centralised and vertical hierarchical forms of regulation to decentralised, horizontal and networked forms: this is a phenomenon claimed by some to be global (Rosenau, 1999) though this is hotly disputed, both pre- and post the recent global pandemic. However, whatever the extent of variation, governance is described in ways that reflect broad patterns that themselves may be understood to discursively reflect dominant political forces. The increasing involvement of private actors in the production of knowledge in education has multiplied these effects, since the emergent ‘stakeholderisation’ of global governance has led to diminished rather than increased democratic decision-making.

As the chapters in this book will discuss, novel governing practices promote ways of controlling and shaping behaviour (Hood et al., 2001) that mix material and discursive strategies: the discursive mobilisation of new norms and values is combined with external regulatory mechanisms (for example, competitive indicators of performance or global monitoring regimes, such as the SDGs) which together seek to transform the conduct of organisations and individuals. As a result, transnational governance is produced through the construction of ever-evolving epistemic infrastructures where the technical and the political have become a single entangled mix: as will be discussed in the chapters of this book, in the name of the democratisation of knowledge production, the so-called pluralism of voices has paradoxically led to the further strengthening of monodisciplinary and datafied knowledge for governance (Grek, 2022; Tichenor et al., 2022).

In many European countries, we can trace a process of circulation of these discursive norms from the 1980s, and the simultaneous development of new regulatory forms: we observe deregulation accompanied by tighter specification (for example, in the field of education, the emergence of centrally prescribed curricula and testing regimes), the growth of technical accountability, and a dominance of new public management principles applied to the public sector. In education in particular, there has been a steady growth of governing through performance management around principles of decentralisation, devolution and deregulation as key principles of system restructuring (Whitty et al., 1998). Those key principles were not challenged—indeed, in many cases, they were reinforced—by shifts in political parties in power in most European countries, and indeed in the governance of the European project itself, as new ‘imaginaries’ (Jessop, 2008) connected education closely with the rise of knowledge economies for improving growth and social cohesion (Mulderrig, 2008).

In the same timeframe, we can also chart the emergence of apparently new forms of knowledge that provide useful support for agendas that stress collaborative solutions and rapid adaptation, or that express ‘new institutional compatibilities’ (Nowotny et al., 2001), between knowledge production and use. In the era of neoliberalism, knowledge became internal, i.e. part of, rather than external to and distinct from the economic process. Economic growth was seen as dependent on maximising the outputs of knowledge workers and the productivity of knowledge resources. National education systems sought to ensure competitive advantage through the commercial exploitation and application of the knowledge produced by ‘research-intensive’ universities. Technologies enabled the instantaneous exchange of information. These exchanges transcend national boundaries, so the constraints of national economies give way to an interdependent global economy; the recent pandemic has accelerated the process of digitisation of education further. The funding, organisation and assessment of research quality are all affected by these developments. Kenway et al. (2004) illustrate the trend towards prioritising techno-scientific research and its modes of operation and organisation, so that research is increasingly concentrated in designated centres of excellence, organised in teams and characterised by differences in conditions of work and employment rights. Traditional intellectual autonomy is challenged by the need to meet industry needs and, as a consequence, science is becoming less a public good than more of a tradable commodity.

The centrality of research and knowledge production for growth helps to explain the enhanced research steering policy agendas across different national settings in Europe and beyond. Research steering processes emerge at the national level that promote particular methodologies and particular forms of measurement of research quality and recognition (for example various forms of metrics, benchmarking and citation indices). In addition, knowledge has further been commodified, through the emergence of a large data production and information industries, which is described by policymakers as promising greater transparency and hence quality for the public services, education included (Ball, 2007). These trends reflect a perspective on education research that prioritises its ‘use-value’ and its problem-solving potential for policymakers, as key indicators of quality.

This increased significance of knowledge means that in the developed world, information and expertise have—for some time now—been more widely available and more widely distributed than ever before. At the same time, new governance forms promote the idea of transparency, public accountability, sustainability and the democratisation of knowledge as part of their strategic positioning; decoloniality has become the rallying cry of those who fight against the continued epistemic injustices of a knowledge system organised and decided upon by the Global North. Knowledge is drawn into supporting the legitimacy and authority of the social and political processes of new governance agendas. Discursively, knowledge and policy are produced as a form of cultural political economy (Jessop, 2008) which combines semiotic and material elements in changing the nature of knowledge production and its role in governing. Policymakers suggest that social cohesion and effective government now depend on integrating knowledge in decision-making processes. This positioning promotes an agenda for the future in which potentially disruptive energies (the rise of artificial intelligence, for example) are harnessed to promote a discourse of entrepreneurship and continuous scientific and technical advancements (Mulderrig, 2008, p. 167). As Bauman (1992) put it three decades ago, in a decentred, information-rich society, governance needs to use ‘science’ more actively to minimise risk, or—at the very least—to minimise anxiety about risk.

The production of expert knowledge in education is subjected to the same forms of regulation and risk management. As with other expert knowledge, it is applied, scientised knowledge, packaged in flows of data and tables. Knowledge production is equated with particular forms of data collection and comparison and its quality is judged in relation to its usefulness in assessing comparative performance. This transformation of the field of education is happening through the reshaping of the old institutions of schooling and post-compulsory education and their replacement with designs for (lifelong) learning, that require new, accessible and portable qualifications frameworks (Grek, 2008; Grek & Russell, 2023; Ozga et al., 2006) and through the development of new attitudes that instil responsibility and commitment to continuous self-improvement for schools and learners alike. The task of governing knowledge is to map and loosely link a complex space of flows of international and national actors and data, with the aim of imposing its logic over scattered, segmented places or what Martin Lawn previously called ‘systemless’ systems (2013): in other words, the disarticulation of a public education system into political, spatial, contextual and increasingly commercial parts is loosely connected via data.

In most education systems around the world, systems of performance and quality management have learnt to provide ‘proof’ of the quality of their ‘outputs’. Middle class parents became experts in decoding and using this information, while policymakers are more dependent on and subject to the judgements of experts. Quality management regimes or the various systems of research quality assessment are input–output machines that contain team rules, rules of evaluation, cooperation and innovation. Experts propagate ‘efficiency myths’ that allow for the growth of quality management and professionals, including the teaching profession and academic researchers are reformed as active protagonists of quality systems. In this process, we see the ‘transformation’ rather than the transfer of knowledge, with the key element of scientific knowledge production—uncertainty—simultaneously (or, better, temporarily) removed and strengthened so as to necessitate the continued need for the further production of data (Stehr, 1994). The elimination of doubt and continuous affirmation of usefulness and ‘social impact’ are constructed discursively through the language of research assessment. Experts are ‘chosen’ for their capacity to provide what they often see or translate into technical advice and—increasingly—the presence of experts in such ‘user-driven’ activities counts as a quality indicator in itself (Lawn & Lingard, 2002).

To conclude, quantification and the production of expertise more broadly do not merely inform but have come to constitute a ‘state optic for governing’ (Scott, 1998). There are intimate and interwoven relationships between the development of state administrative structures, characterised by Latour (1987) as ‘centres of calculation’, and the development of standardisation, methodological approaches, technologies and related cognitive schemes of statistics and scientific thinking (Desrosières, 1998; Porter, 1995). This analysis is, of course rather at odds with the collaborative and socially embedded possibilities of co-production of knowledge as presented by Nowotny and her colleagues (2001, 2003), and runs against more recent developments around the decolonisation and democratisation of knowledge production more generally and expert knowledge for policy production in particular. The next section will briefly discuss Mode 2 knowledge production, before moving to a description of the book’s chapters and the ways the Mode 2 regime has further transformed as a result of the evolution of quantification.

4 From Mode 1 Mode 2 Science

The literature on new modes of knowledge production gained traction in the 1990s and 2000s as global challenges began emerging and presenting multiple elements of complexity and intertwinement (Crowley & Head, 2017). Therefore, it quickly became obvious (to some, at least) that new kinds of expert knowledge needed to be produced in order to deal with those global social transformations (Eyal, 2019). The central rationale underpinning this body of work in general (Etzkowitz & Leydesdorff, 2000; Funtowicz & Ravetz, 1993; Wesselink & Hoppe, 2011) and the concept of Mode 2 (Gibbons et al., 1994; Nowotny et al., 2001) in particular, was an observation that a change in societal values and practices inevitably results in an evolution of the epistemic structures of such society. Therefore, scholars working in the field of Science and Technology Studies understood transformations in governing—and particularly the shift from government to governance—as leading to changes to the systems of knowledge production (Miller & Rose, 2008).

As a result, The New Production of Knowledge: The Dynamics of Science and Research in Contemporary Societies published by Michael Gibbons and colleagues in 1994 argues that a new model of knowledge production has emerged in modern societies (a summary is presented in Table 1). According to Gibbons et al. (1994), the increasing complexity of societal issues had posed a challenge to academic knowledge production, leading to the replacement of traditional science (Mode 1) with new knowledge production (Mode 2).

Table 1 Attributes of Mode 1 and Mode 2 science (based on Gibbons et al., 1994)
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First, in contrast to older paradigms and practices, knowledge production in the late twentieth century was seen as deeply embedded in society, rather than limited to universities: knowledge, it was argued, could not be solely produced in ivory towers any longer but had to take the ‘context of application’ into account. What STS scholars called ‘contextualised science’ assumed a deep embeddedness of science within society, whereby society in turn ‘speaks back’ to science (Gibbons et al., 1994, p. 50). Second, the new knowledge was seen as produced outside of the traditional disciplinary boundaries, as the new challenges require collaborations between experts with diverse disciplinary and institutional backgrounds (Gibbons et al., 1994). Consequently, both the empirical and the theoretical structure of knowledge produced within the Mode 2 differs from the traditional, siloed and disciplinary structures. Third, knowledge production went beyond the traditional academic structures and was made by a variety of actors, including government agencies, research centres, think tanks, international organisations and others. Fourth, the new knowledge production was seen as more responsive and reflexive to societal needs; the supremacy of social problems, versus the notion of scientific autonomy as the central cultural value of science, is a defining feature of science for society rather than the production of science for science’s sake. Lastly, in the Mode 2 transformation, the quality of knowledge production is assessed by using the broader criteria of social, economic and cultural usability of the produced knowledge (Gibbons et al., 1994).

The concept of Mode 2 science gained considerable traction and moved beyond the production of scientific knowledge to also cover policy knowledge (Logar, 2011; Lövbrand, 2011; Raftery et al., 2016). Despite its intuitive appeal for explaining the changes in the production of knowledge for policy (Yearley, 2005), the work of Gibbons et al. (1994) sparked a broad debate. The Mode 2 concept was criticised for the lack of theoretical grounding (to which the authors responded in the second book—Nowotny et al., 2001), as well as an ahistorical view of the evolution of science. Thus, the critique of Mode 2 science highlighted the fact that the notion of science devoid of any practical consideration—the central quality of Mode 1—is very rare when seen from the historical perspective (for example Etzkowitz & Leydesdorff, 2000; Pestre, 2003).

However, the main criticism against Mode 2 revolved around what was perceived as an evolutionary perspective on knowledge-making, hence ignoring the political and social shaping of knowledge production in contemporary societies (Pestre, 2003). As Dominique Pestre points out, analysis of the discourse used to characterise Mode 2 knowledge (for example social relevance, responsibility, reflexivity, fluidity) largely eliminated alternative definitions of knowledge and knowledge production and created a strong normative pressure on researchers to enhance their responsiveness and usefulness. As he put it, the Mode 2 discourse conveys an ‘overly optimistic’ vision of the changes affecting science and society today. He goes on to comment that:

The authors may have underestimated the extent to which these transformations have been the result of political and social choices. This would mean recognising that the developments they describe are not cases of natural evolution, which have simply to be identified and acknowledged, but are, rather, articulated with alternative and conflicting social, economic and political projects. (Pestre, 2003, p. 246, emphasis in original)

As well as reminding us of the ways in which knowledge has always mattered to states and economic elites, Pestre underlines the importance of knowledge as a resource for changing social ideologies (ibid:250). The transformation of knowledge is linked to the transformation of capitalism in this analysis, showing how knowledge has both mirrored that shift and made it possible, thus creating new levels of interdependence, of the kind illustrated earlier in the discussion of performance data. This interdependence is also neatly captured in Nigel Thrift’s book Knowing Capitalism (Thrift, 2005), which illustrates how the cultural circuit of capitalism produces knowledge about itself and illuminates how capitalism has become knowledgeable and thus increasingly impinges on traditional academic preserves (Thrift, 2005, p. 21). Part of this process, Thrift argues, involves capital and traditional knowledge producers in the academy coming to ‘think more alike about thinking’ (ibid., p. 21).

Indeed, as the chapters of this book will discuss, the METRO research has shown that the production of expert knowledge for policy, at least at the transnational realm, has increased the universality and often uniformity of choices and outcomes of education policy-making, often leading to a much closer alignment of education with the economy. As a result, one of the key functions of experts is the brokerage of knowledge, in order to find ways to create consensus and—paradoxically—transform the technocratic spaces of number-making to the ones that will also address the democratic deficit that such processes have been blamed for in the past. Thus, rather than the Mode 2 proclamation of hybridity and diversification of knowledge production, what we observe is increased universality of the policy agenda, as well as the technicisation of political issues through the transformation of the spaces of measurement into spaces of negotiation and political consensus-making.

Further, as we will see, rather than choosing trans- or even interdisciplinarity, the mono-disciplinarity of the dominance of economics has prevailed in the field not only of education research but also knowledge production for policy more broadly. In addition, despite Nowotny’s argument about Mode 2 knowledge moving away from hierarchy of knowledge-making as pushed on by the state or the markets, we observe that new markets of measurement and indicators have emerged: the use and predominance of certain measurements over others determine their popularity and lead policymakers to react to them differently and often frenetically (as the PISA experience widely showed). In addition, as we have seen, not only do ‘governance’ and ‘mode 2 knowledge’ share a repertoire of defining terms, but they have also worked discursively to create images of progress and democratisation, to support inclusion, and to push for the co-option of knowledge in governance, dissolving the boundaries between them. The so-called governance turn is often defined in terms that echo this supposed transformation of knowledge from elitist to more democratic: a shift from centralised and vertical hierarchical forms of regulation to decentralised, horizontal, networked forms. Yet, thirty years on, rather than representing the potential for democratisation of either knowledge or governance, these forms have come to closely resemble the networking practices and open communications systems of global capital. Indeed, ‘edu-businesses’ are now also a key knowledge actor and a significant player in education systems around the world, especially after the global pandemic. Education research for policy often reflects the processes and instruments of knowledgeable capitalism and its dominant ‘economic imaginaries’ have established new organisational and numerical forms that have ‘a performative, constitutive force’ (Jessop, 2008:18).

Taking all these serious and valid criticisms of the Mode 1 to Mode 2 schema of knowledge transformation, as well as more recent moves to discuss a Mode 3 paradigm (which further legitimise and reinforce the critique against the normative character of Mode 2—see, for example, Carayannis & Campbell, 2011), The New Production of Expert Knowledge: Education, Quantification and Utopia utilises the schematic representation of Mode 2 to discuss expert knowledge production in the first quarter of the twenty-first century (Table 2).

Table 2 From Modes 1 and 2 to the new expert knowledge production
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The next and last section of this introductory chapter will outline the chapters in this book and explain how they cast light on the new expert knowledge-making.

5 The Book’s Structure

Chapter 2 will discuss the move away from the specificity of the context of application to the universality and interdependence of global education metrics: as I shall show, instead of the production of contextual knowledge, quantification in transnational governance has led to the production of expertise that is thoroughly standardised, de-contextualised, interdependent and even universal. The chapter focuses on two empirical examples of international organisations that saw their status as knowledge producers and expert brokers rise over the last 20 years: these are the OECD, and its collaboration with the European Commission, as well as the UNESCO Institute of Statistics with its coordination of the SDG4. Through an analytical account of these organisations’ key measurement exercises, the chapter charts two key developments towards the production of decontextualised governing knowledge: these are the rise of the interdependence of IOs in the production of expertise; and secondly, the production of universal narratives of education progress and unity.

Chapter 3 analyses the ways that the production of data for education over the last three decades, despite the complexity and interdependency of policy problems in education, has not been interdisciplinary, but the opposite: it has primarily been dependent on the discipline of economics and the ensuing economisation of education policy as the preferred mode of producing knowledge for governing. The chapter mobilises relevant literature and uses empirical examples in order to offer two propositions: first, that instead of disciplinarity, global education governance is primarily dependent on a monodisciplinary knowledge production orientation; second, that an investigation of metrological realism needs to focus on the social construction of non-knowledge as a vital component of studying the epistemic authority of transnational institutions.

Chapter 4 turns the lens to the processes that influence and steer the production of expert knowledge in the global governance of education over the last 50 years. The chapter adopts the position that its construction is not ‘organic’—the product of traditional knowledge-making as it became dominant from the Enlightenment onwards—but rather the outcome of complex undertakings that often imbricate a wide variety of actors—both national and international, including decision-makers—and different fields. The chapter builds on the shift from Mode 1 to Mode 2 knowledge production (Gibbons et al., 1994) in order to document further changes to how expert knowledge is produced today: it argues that, at least in the field of global education governance, we see concerted efforts to produce expert knowledge that focuses equally on technocratic and political accountability, and that sees brokerage and consensus-making as the ultimate goals in an increasingly polarised and uncertain post-pandemic world.

Chapter 5 focuses on an analysis of the role of storytelling and reflexivity in further strengthening and legitimising quantification in global education governance. It examines two specific empirical examples that show, first, how data visualisations in education and sustainable development are changing in order to accommodate the construction of a more democratic and inclusive governing space; second, how policy and expert actors themselves use reflexivity as a way not only to understand and think about their daily policy work, but also to create spaces of alignment and consensus. In that way, both storytelling and reflexivity can be seen as working instrumentally, enhancing and further embedding the work of ‘governing by numbers’, rather than displacing them.

Chapter 6 discusses the rise of the competition over measurement which has been structuring the relationships between IOs. The production of data to support comparative assessment and evaluation is one of IOs’ key organisational remits, therefore, they have vested interests in promoting the implementation of their measures over those of others. Consequently, what we observe in the global governance of education is not merely ‘governing by numbers’, but rather a navigation of the market of measurement; this can often lead to conflicts and controversies over statistical data collection, as well as new partnerships and collaborations. Thus, it becomes obvious that it is not merely epistemic authority that governs the production of quantification. Rather, a market logic affects the way data are constructed, collected and compared. In this setting, measures are not merely assessed based on their epistemic qualities—for example, how well they capture the reality of higher education—but rather in their ‘market share’, i.e. the number of countries and agencies agreeing to participate and contribute to the work of measurement. In this chapter, we move away from the global level and examine the case of quality assurance in higher education in Europe in order to substantiate how, why and with effects this market of measurement works.

Finally, the book’s concluding Chapter 7 brings together the five different strands of the empirical and theoretical analysis, in order to argue for a novel perspective of the role of quantification in the production of education future utopias. The chapter discusses the ways that metrological realism has constructed a well-supported epistemic infrastructure, built on relationships and practices that go beyond the mere objectivity and reliability of numerical evidence; rather, quantification has become the new political imagination of planning and executing future education governing vistas.