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 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.