From indicators to indicating interdisciplinarity: A participatory mapping methodology for research communities in-the-making

Noortje Marres,Sarah De Rijcke

doi:10.1162/qss_a_00062

Abstract

This article discusses a project under development called “Inventing Indicators of Interdisciplinarity,” as an example of work in methodology development that combines quantitative methods with interpretative approaches in social and cultural research. Key to our project is the idea that Science and Technology Indicators not only have representative value, enabling empirical insight into fields of research and innovation but simultaneously have organizing capacity, as their deployment enables the curation of communities of interpretation. We begin with a discussion of concepts and methods for the analysis of interdisciplinarity in Science and Technology Studies (STS) and scientometrics, stressing that both fields recognize that interdisciplinarity is contested. To make possible a constructive exploration of interdisciplinarity as a contested—and transformative—phenomenon, we sketch out a methodological framework for the development and deployment of “engaging indicators.” We characterize this methodology of indicating as participatory, abductive, interactive, and informed by design, and emphasize that the method is inherently combinatory, as it brings together approaches from scientometrics, STS, and humanities research. In a final section, we test the potential of our approach in a pilot study of interdisciplinarity in AI, and offer reflections on digital mapping as a pathway towards indicating interdisciplinarity.

Highlights

The ambition to move beyond the divide between quantitative and qualitative methods is a familiar one in the social studies of science and technology (STS)
In a series of meetings and workshops, we reviewed the capacities of methods developed in diverse fields— scientometric analysis of Web of Science, social media analysis, and playful mapping—to indicate interdisciplinarity in a specific area of research and innovation and on this basis, we identified key features and requirements for what we here call engaging indicators
We reflect on what is at stake in this project for us as STS scholars working in interpretative traditions, and briefly indicate the methodological potential of engaging indicators through a discussion of our shared efforts to indicate interdisciplinarity in artificial intelligence (AI)

Summary

INTRODUCTION

The ambition to move beyond the divide between quantitative and qualitative methods is a familiar one in the social studies of science and technology (STS). In a series of meetings and workshops, we reviewed the capacities of methods developed in diverse fields— scientometric analysis of Web of Science, social media analysis, and playful mapping—to indicate interdisciplinarity in a specific area of research and innovation (artificial intelligence) and on this basis, we identified key features and requirements for what we here call engaging indicators. On this basis, we here outline our proposal for a combinatory methodology for the development of engaging indicators. We reflect on what is at stake in this project for us as STS scholars working in interpretative traditions, and briefly indicate the methodological potential of engaging indicators through a discussion of our shared efforts to indicate interdisciplinarity in AI

INTERDISCIPLINARITY AS A CATEGORY AT STAKE

Note that mapping does not necessarily presume flat ontology

FROM INDICATORS TO INDICATING INTERDISCIPLINARITY

TEST CASE

CONCLUSION