Ethics review for international data-intensive research
Bibliographic record
Abstract
Historically, research ethics committees (RECs) have been guided by ethical principles regarding human experimentation intended to protect participants from physical harms and to provide assurance as to their interests and welfare. But research that analyzes large aggregate data sets, possibly including detailed clinical and genomic information of individuals, may require different assessment. At the same time, growth in international data-sharing collaborations adds stress to a system already under fire for subjecting multisite research to replicate ethics reviews, which can inhibit research without improving the quality of human subjects' protections ( 1 , 2 ). “Top-down” national regulatory approaches exist for ethics review across multiple sites in domestic research projects [e.g., United States ( 3 , 4 ), Canada ( 5 ), United Kingdom, ( 6 ), Australia ( 7 )], but their applicability for data-intensive international research has not been considered. Stakeholders around the world have thus been developing “bottom-up” solutions. We scrutinize five such ef orts involving multiple countries around the world, including resource-poor settings (table S1), to identify models that could inform a framework for mutual recognition of international ethics review (i.e., the acceptance by RECs of the outcome of each other's review).
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How this classification was reachedexpand
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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| 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 teacher head, 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".