Article | Open Access
Algorithmic Allocation: Untangling Rival Considerations of Fairness in Research Management
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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
Published:
Issue:
Vol 8, No 2 (2020): Quantifying Higher Education: Governing Universities and Academics by Numbers
© Guus Dix, Wolfgang Kaltenbrunner, Joeri Tijdink, Govert Valkenburg, Sarah de Rijcke. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 license (http://creativecommons.org/licenses/by/4.0), which permits any use, distribution, and reproduction of the work without further permission provided the original author(s) and source are credited.