Revised CIOMS research ethics guidance: on the importance of process for credibility
Bibliographic record
Abstract
This paper reviews the 2016 CIOMS International Ethical Guidelines for Health-related Research Involving Humans. I argue that these new guidelines constitute a significant improvement over the guidelines they replace. However, the procedures put in place by CIOMS resulted in an authoring group consisting of a majority of authors and advisors hailing from the global North, while the guidelines squarely aim at influencing policies in the global South. I question CIOMS' strategy to produce a consensus based document, and raise concerns about frequent appeals to authority designed to establish the credibility of these guidelines and the processes that led to them. It is unclear why it should be the role of a small organisation such as CIOMS to try to guide the research ethics policies in countries of the global South.
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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 | 0.660 | 0.822 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.012 | 0.057 |
| Scholarly communication | 0.040 | 0.031 |
| Open science | 0.012 | 0.024 |
| Research integrity | 0.046 | 0.084 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".