Critical Prison Research and University Research Ethics Boards: Homogenization of Inquiry and Policing of Carceral Knowledge
Bibliographic record
Abstract
This article illustrates how authoritative regulatory practices that research ethics boards may deploy when assessing non-traditional social research may pave the way to a homogenization of inquiry and forms of policing of knowledge. The authors sought institutional ethics clearance from multiple research ethics boards in the case of a critically-oriented participatory action-based study with formerly incarcerated persons in Canada. Evidence is provided from two case studies. Two unexpected challenges were encountered from research ethics board members. The first challenge was related to the board’s stereotypical bias about the violent potential of former prisoners (as co-researchers and participants). The second challenge was related to an overly cautious interpretation of federal ethical guidelines leading to the exclusion of Indigenous peoples from the project. Both challenges have in common that they point to research ethics boards’ possible role in the policing of knowledge which may jeopardize researchers’ ability to engage in critical scholarship. El artículo ilustra las prácticas regulatorias autoritarias que los comités de ética en investigación pueden adoptar, y cómo éstas allanan el camino a la homogeneización de las indagaciones y a la vigilancia sobre la producción de conocimiento. Se exhiben pruebas de dos estudios de caso en los cuales los miembros del comité se toparon con situaciones que no esperaban. La primera estaba relacionada con el sesgo típico de los comités sobre el potencial violento de antiguos reclusos, y la segunda, con una interpretación excesivamente prudente de directrices éticas federales, lo cual desembocó en la exclusión de personas indígenas del proyecto. Ambas situaciones señalan al posible rol de los comités de ética como vigilantes de la producción de conocimiento, algo que podría poner en peligro la capacidad de los investigadores para realizar una labor académica crítica. Available from: https://doi.org/10.35295/osls.iisl/0000-0000-0000-0931
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.238 | 0.240 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.034 | 0.135 |
| Scholarly communication | 0.034 | 0.024 |
| Open science | 0.004 | 0.033 |
| Research integrity | 0.008 | 0.010 |
| 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".