Beyond Consent: Respect for Community in Genetic Research
Bibliographic record
Abstract
Abstract Paralleling the broadening of scientific thought occasioned by the human genome project, calls have been sounded to expand research ethics to include a principle of ‘respect for community’ in genetic research. The principle is responsive to a history of genetic research that has harmed some groups. The principle recognises that communities hold dignitary interests, values and rights. For such reasons, it has gained recognition in national and international health research ethics norms. To help translate respect for community into research practice, we identify selected ethics elements and research approaches, including: collaborative community research; jointly defining research priorities and questions; informed consent; joint interpretation and dissemination of results; community ethics deliberations and fair benefit sharing. Implementing such elements presents challenges that merit interdisciplinary study, pluralistic debate and analysis. With such work, we project a future with fuller recognition of respect for community as an ethical principle and duty in human research ethics. Key Concepts: Evolving research ethics requires protection of communities, in addition to protection of individuals – this also applies to genetic research. Protection of communities supports the ethical concept of respect for communities. National and international research ethics guidelines support these concepts. Respect for communities can be achieved through researchers using collaborative research with communities. Collaborative research uses principles and practice from both participatory research and community‐engaged research. Collaborative research maximises benefits and minimises harms for communities and groups within the communities. Collaborative research impacts researchers, communities, institutional and community ethics boards and those publishing the results.
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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 | 0.311 | 0.358 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.099 |
| Scholarly communication | 0.021 | 0.023 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.019 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 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; a candidate call from one source (direct Gemma or distilled Codex), 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".