Rethinking ethical reflexivity and oversight in health research through an ecosystem approach: A workshop report
Bibliographic record
Abstract
As the scope of morally relevant considerations widens and new challenges emerge at the frontiers of health innovation, there are questions about the appropriate role and remit for research ethics review, within the broader context of the whole health research ecosystem. Drawing on discussion at a satellite meeting at the 2022 Global Forum on Bioethics in Research in Cape Town, we argue that the ethical conduct of research is the responsibility of all stakeholders in the research ecosystem – from funders, governments and research institutions to individual research teams and ethics committees. As a research community we need to espouse, and take action to achieve, more distributed approaches to ethical scrutiny and reflexivity. A crucial element of such a shift should be the development of collaborative and non-adversarial relationships between researchers and ethics committees that recognise and respect the mutual responsibilities of all parties to promote ethical research conduct. In tandem with the development of systems to support the exercise of ethical responsibilities across the research ecosystem, committees need to reconceptualise their role, in partnership with communities, as one of providing accountability through a focus on how research promotes participant agency and the common good.
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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 | 0.219 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.024 | 0.021 |
| Open science | 0.006 | 0.033 |
| Research integrity | 0.017 | 0.027 |
| Insufficient payload (model declined to judge) | 0.004 | 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".