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 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.