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Algorithmic Allocation: Untangling Rival Considerations of Fairness in Research Management

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

Making researchers responsible: attributions of responsibility and ambiguous notions of culture in research codes of conduct

BACKGROUND: Research codes of conduct offer guidance to researchers with respect to which values should be realized in research practices, how these values are to be realized, and what the respective responsibilities of the individual and the institution are in this. However, the question of how the responsibilities are to be divided between the individual and the institution has hitherto received little attention. We therefore performed an analysis of research codes of conduct to investigate how responsibilities are positioned as individual or institutional, and how the boundary between the two is drawn. METHOD: We selected 12 institutional, national and international codes of conduct that apply to medical research in the Netherlands and subjected them to a close-reading content analysis. We first identified the dominant themes and then investigated how responsibility is attributed to individuals and institutions. RESULTS: We observed that the attribution of responsibility to either the individual or the institution is often not entirely clear, and that the notion of culture emerges as a residual category for such attributions. We see this notion of responsible research cultures as important; it is something that mediates between the individual level and the institutional level. However, at the same time it largely lacks substantiation. CONCLUSIONS: While many attributions of individual and institutional responsibility are clear, the exact boundary between the two is often problematic. We suggest two possible avenues for improving codes of conduct: either to clearly attribute responsibilities to individuals or institutions and depend less on the notion of culture, or to make culture a more explicit concern and articulate what it is and how a good culture might be fostered

Expanding Research Integrity: A Cultural-Practice Perspective

Research integrity (RI) is usually discussed in terms of responsibilities that individual researchers bear towards the scientific work they conduct, as well as responsibilities that institutions have to enable those individual researchers to do so. In addition to these two bearers of responsibility, a third category often surfaces, which is variably referred to as culture and practice. These notions merit further development beyond a residual category that is to contain everything that is not covered by attributions to individuals and institutions. This paper discusses how thinking in RI can take benefit from more specific ideas on practice and culture. We start by articulating elements of practice and culture, and explore how values central to RI are related to these elements. These insights help identify additional points of intervention for fostering responsible conduct. This helps to build “cultures and practices of research integrity”, as it makes clear that specific times and places are connected to specific practices and cultures and should have a place in the debate on Research Integrity. With this conceptual framework, practitioners as well as theorists can avoid using the notions as residual categories that de facto amount to vague, additional burdens of responsibility for the individual

Making researchers responsible: attributions of responsibility and ambiguous notions of culture in research codes of conduct

Background: Research codes of conduct offer guidance to researchers with respect to which values should be realized in research practices, how these values are to be realized, and what the respective responsibilities of the individual and the institution are in this. However, the question of how the responsibilities are to be divided between the individual and the institution has hitherto received little attention. We therefore performed an analysis of research codes of conduct to investigate how responsibilities are positioned as individual or institutional, and how the boundary between the two is drawn. Method: We selected 12 institutional, national and international codes of conduct that apply to medical research in the Netherlands and subjected them to a close-reading content analysis. We first identified the dominant themes and then investigated how responsibility is attributed to individuals and institutions. Results: We observed that the attribution of responsibility to either the individual or the institution is often not entirely clear, and that the notion of culture emerges as a residual category for such attributions. We see this notion of responsible research cultures as important; it is something that mediates between the individual level and the institutional level. However, at the same time it largely lacks substantiation. Conclusions: While many attributions of individual and institutional responsibility are clear, the exact boundary between the two is often problematic. We suggest two possible avenues for improving codes of conduct: either to clearly attribute responsibilities to individuals or institutions and depend less on the notion of culture, or to make culture a more explicit concern and articulate what it is and how a good culture might be fostered

Algorithmic Allocation: Untangling Rival Considerations of Fairness in Research Management

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