Who owns your consent? How REBs give away participants’ agency
Bibliographic record
Abstract
We draw on three illustrative vignettes to examine how REBs manage participants’ agency in the context of qualitative research. We ask: Who owns a participant’s consent? Central to informed consent is the principle of Respect for Persons , which privileges the autonomy of individuals to make decisions about what happens (or not) to them. Yet, REBs sometimes require researchers to get permission from organizations to conduct research on their current and former members, even when the research is not about those organizations. Our aim is to raise awareness about the inherent contradictions of this practice and to consider guidelines for determining the appropriateness of involving organizations that may be tangentially connected to the research objectives or potential participants.
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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 | 0.187 | 0.317 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.054 |
| Scholarly communication | 0.016 | 0.027 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.017 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".