Resisting the seduction of “ethics creep”: Using Foucault to surface complexity and contradiction in research ethics review
Bibliographic record
Abstract
In this paper we examine "ethics creep", a concept developed by Haggerty (2004) to account for the increasing bureaucratization of research ethics boards and institutional review boards (REB/IRBs) and the expanding reach of ethics review. We start with an overview of the recent surge of academic interest in ethics creep and similar arguments about the prohibitive effect of ethics review. We then introduce elements of Michel Foucault's theoretical framework which are used to inform our analysis of empirical data drawn from a multi-phase study exploring the accessibility of community-engaged research within existing ethics review structures in Canada. First, we present how ethics creep emerged both explicitly and implicitly in our data. We then present data that demonstrate how REB/IRBs are experiencing their own form of regulation. Finally, we present data that situate ethics review alongside other trends affecting the academy. Our results show that ethics review is growing in some ways while simultaneously being constrained in others. Drawing on Foucauldian theory we reframe ethics creep as a repressive hypothesis which belies the complexity of the phenomenon it purports to explain. Our discussion complicates ethics creep by proposing an understanding of REB/IRBs that locates them at the intersection of various neoliberal discourses about the role of science, ethics, and knowledge production.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.254 | 0.309 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.004 |
| Science and technology studies | 0.022 | 0.241 |
| Scholarly communication | 0.019 | 0.033 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.011 | 0.020 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".