Assessing Risk to Researchers: Using the Case of Sexuality Research to Inform Research Ethics Board Guidelines
Bibliographic record
Abstract
Research Ethics Boards (REBs) typically focus on ensuring the safety of participants. Increasingly, the risk that research poses to researchers is also discussed. Should REBs involve themselves in determining the degree of allowable researcher risk, and if so, upon what should they base that assessment? The evaluation of researcher safety does not appear to be standardized in any national REB protocols. The implications of REB review of researcher risks remain undertheorized. With a critical queer framework, we use the example of sexuality research to illustrate problems that could arise if researcher risk is assessed. We concentrate on two core research ethics guidelines: 1. How research risk compares to the risks of everyday life. 2. How potential harms compare to the anticipated research benefits. Some argue that sexuality research is more deeply scrutinized than research in other fields, viewed as inherently risky for both participants and researchers. The example of sexuality research helps make explicit the moral undertones of procedural ethics. With these moral undertones in mind, we argue that if adopted, researcher risk guidelines should be the purview of pedagogical relationships or workplace safety requirements, not REBs. Any risk training should be universally required regardless of the research area.
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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 | 0.474 | 0.473 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.017 | 0.093 |
| Scholarly communication | 0.024 | 0.045 |
| Open science | 0.009 | 0.021 |
| Research integrity | 0.047 | 0.035 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".