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.
Highlights
Large research organizations need to address a question that is at once moral and technical: How to allocate resources across different departments to effectively foster certain organizational goals and be perceived as generally fair at the very same time
This article focuses on higher education as one of the key areas of social life where market-oriented practices, quantitative performance indicators, and algorithmbased decision-making processes have made large inroads
We addressed an analytical question central to higher education as it is managed and experienced nowadays: What kinds of normative consideration do researchers and administrators draw upon to justify or challenge algorithm-based allocation? To answer that question, we untangled two rival notions of fairness that were at play in research management
Summary
Algorithmic allocation, as we call it, is a form of quantitative reasoning that makes it possible to calculate the costs and the achievements of a department without engaging in a detailed qualitative assessment of the current content and future potential of its research activities. This article focuses on higher education as one of the key areas of social life where market-oriented practices, quantitative performance indicators, and algorithmbased decision-making processes have made large inroads. More it extends the literature discussed above in the direction of the normative considerations that people offer to support or problematize these developments. Based on our analytic distinction, we caution against having a narrow perspective of research organizations as being relatively homogenous entities and mere recipients of broader trends in academia, and we offer a set of questions to enable researchers and research managers to think reflexively about algorithm-based decision-making (Section 6)
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