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Algorithmic Allocation: Untangling Rival Considerations of Fairness in Research Management

Author

Listed:
  • 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)

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.

Suggested Citation

  • Guus Dix & Wolfgang Kaltenbrunner & Joeri Tijdink & Govert Valkenburg & Sarah de Rijcke, 2020. "Algorithmic Allocation: Untangling Rival Considerations of Fairness in Research Management," Politics and Governance, Cogitatio Press, vol. 8(2), pages 15-25.
    • Handle: RePEc:cog:poango:v:8:y:2020:i:2:p:15-25
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    References listed on IDEAS

    as
    1. Jean-Claude Thoenig & Catherine Paradeise, 2018. "Higher Education Institutions as Strategic Actors," Post-Print hal-01690642, HAL.
    2. Thoenig, Jean-Claude & Paradeise, Catherine, 2018. "Higher Education Institutions as Strategic Actors," European Review, Cambridge University Press, vol. 26(S1), pages 57-69, February.
    3. Alexander D. Rushforth & Sarah de Rijcke, 2017. "Quality monitoring in transition: The challenge of evaluating translational research programs in academic biomedicine," Science and Public Policy, Oxford University Press, vol. 44(4), pages 513-523.
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    Cited by:

    1. Julian Hamann, 2020. "Governance by Numbers: A Panopticon Reversed?," Politics and Governance, Cogitatio Press, vol. 8(2), pages 68-71.
    2. Dukes, Ruth & Streeck, Wolfgang, 2020. "From industrial citizenship to private ordering? Contract, status, and the question of consent," MPIfG Discussion Paper 20/13, Max Planck Institute for the Study of Societies.
    3. Maarten Hillebrandt & Michael Huber, 2020. "Editorial: Quantifying Higher Education: Governing Universities and Academics by Numbers," Politics and Governance, Cogitatio Press, vol. 8(2), pages 1-5.

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