Recognizing Risk and Vulnerability in Research Ethics: Imagining the “What Ifs?”
Bibliographic record
Abstract
Research ethics committees (RECs) may misunderstand the vulnerability of participants, given their distance from the field. What RECs identify as the vulnerabilities that were not adequately recognized in protocols and how they attempt to protect the perceived vulnerability of participants and mitigate risks were examined using the response letters sent to researchers by three university-based RECs. Using a critical qualitative method informed by feminist ethics, we identified an overarching theme of recognizing and responding to cascading vulnerabilities and four subthemes: identifying vulnerable groups, recognizing potentially risky research, imagining the "what ifs," and mitigating perceived risks. An ethics approach that is up-close, as opposed to distant, is needed to foster closer relationships among participants, researchers, and RECs and to understand participant vulnerability and strength better.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | medium |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.716 | 0.880 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.014 | 0.027 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.272 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".