Towards an ethics safe harbor for global biomedical research
Bibliographic record
Abstract
Although increasingly global, data-driven genomics and other 'omics'- focused research hold great promise for health discoveries, current research ethics review systems around the world challenge potential improvements in human health from such research. To overcome this challenge, we propose a 'Safe Harbor Framework for International Ethics Equivalency' that facilitates the harmonization of ethics review of specific types of datadriven international research projects while respecting globally transposable research ethics norms and principles.The Safe Harbor would consist in part of an agency supporting an International Federation for Ethics Review (IFER), formed by a voluntary compact among countries, granting agencies, philanthropies, institutions, and healthcare, patient advocacy, and research organizations. IFER would be both a central ethics review body, and also a forum for review and follow-up of policies concerning ethics norms for international research projects. It would be built on five principle elements: (1) registration, (2) compliance review, (3) recognition, (4) monitoring and enforcement, and (5) public participation. The SafeHarbor would create many benefits for researchers, countries, and the general public, and may eventually have application beyond (gen)omics to other areas of biomedical research that increasingly engage in secondary use of data and present only negligible risks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads 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".