Measuring cultural and ethnic diversity in research and innovation

Cultural and ethnic diversity is increasingly recognised as a driver of creativity and innovation in research and innovation (R&I) systems, yet the metrics employed to measure it often oversimplify or overlook critical dimensions. In this paper, we critically evaluate existing concepts, data collection strategies, and indicators, identifying three pervasive shortcomings: a lack of reflexivity about underlying assumptions and biases, insufficient attention to relative abundance, and inadequate consideration of proximity/similarity. To address these issues, we present a context-specific operationalisation of the Leinster and Cobbold (2012) framework for measuring cultural and ethnic diversity in R&I, which integrates richness, relative abundance and similarity. We demonstrate the practical utility of this index through an illustrative stress-test case study of UK university research communities using name-based inference data, revealing how it offers substantial information gain compared to conventional measures. However, the index remains sensitive to methodological choices, underscoring the need for context-specific applications and critical reflections on data limitations. We conclude by advocating careful use of multidimensional cultural and ethnic diversity metrics, thereby supporting more robust and equitable insights into R&I ecosystems when aligned with appropriate concepts, data sources, and transparent sensitivity analysis.