Algorithmic Allocation: Untangling Rival Considerations of Fairness in Research Management

Guus Dix, Wolfgang Kaltenbrunner, Joeri Tijdink, Govert Valkenburg and Sarah de Rijcke
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Guus Dix: Centre for Science and Technology Studies, Leiden University, The Netherlands
Wolfgang Kaltenbrunner: Centre for Science and Technology Studies, Leiden University, The Netherlands
Joeri Tijdink: Department of Medical Humanities, AmsterdamUMC, The Netherlands / Department of Philosophy, Vrije Universiteit Amsterdam, The Netherlands
Govert Valkenburg: Department of Interdisciplinary Studies of Culture, Norwegian University of Science and Technology, Norway
Sarah de Rijcke: Centre for Science and Technology Studies, Leiden University, The Netherlands

Politics and Governance, 2020, vol. 8, issue 2, 15-25

Abstract: Marketization and quantification have become ingrained in academia over the past few decades. The trust in numbers and incentives has led to a proliferation of devices that individualize, induce, benchmark, and rank academic performance. As an instantiation of that trend, this article focuses on the establishment and contestation of ‘algorithmic allocation’ at a Dutch university medical centre. Algorithmic allocation is a form of data-driven automated reasoning that enables university administrators to calculate the overall research budget of a department without engaging in a detailed qualitative assessment of the current content and future potential of its research activities. It consists of a range of quantitative performance indicators covering scientific publications, peer recognition, PhD supervision, and grant acquisition. Drawing on semi-structured interviews, focus groups, and document analysis, we contrast the attempt to build a rationale for algorithmic allocation—citing unfair advantage, competitive achievement, incentives, and exchange—with the attempt to challenge that rationale based on existing epistemic differences between departments. From the specifics of the case, we extrapolate to considerations of epistemic and market fairness that might equally be at stake in other attempts to govern the production of scientific knowledge in a quantitative and market-oriented way.

Keywords: algorithmic allocation; higher education; marketization; performance indicators; quantification; resource allocation (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:cog:poango:v8:y:2020:i:2:p:15-25

DOI: 10.17645/pag.v8i2.2594

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