Ethics policy as audit in Canadian clinical settings: exiling the ethnographic method
Bibliographic record
Abstract
In the 1990s, institutional research ethics boards (REBs) impacted the relationship between researchers and their participants by formalizing the ethics requirements to conduct research involving human subjects. While exceptions are made in some ethics policies for research that crosses international borders — for example, where obtaining written consent is not culturally acceptable — there are no such exceptions for research that crosses interdisciplinary domains. Consequently, when conducting anthropological fieldwork in a Canadian clinical setting, the requirements of the standardized ethics policy proves to mesh poorly with the ethnographic and general anthropological approach to research, distorting the process and meaning of obtaining informant consent in the anthropological sense. This article documents one neophyte ethnographer's complicated journey into the land of the REB, raising some important questions about the audit-type nature of the ethics review process and the potential exile of ethnography that may result.
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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 | 0.229 | 0.194 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.041 | 0.093 |
| Scholarly communication | 0.022 | 0.012 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".