Participatory Genomic Research: Ethical Issues from the Bottom Up to the Top Down
Bibliographic record
Abstract
Participatory approaches to genomic research manifest along a continuum from bottom-up citizen-science initiatives designed to liberate scientific inquiry from the constraints of traditional research institutional contexts and professional practices to top-down investigator-initiated studies designed to expose the public to scientific research processes and build its support and enthusiasm for genomic research. With foundations as varied as open science, crowdsourcing, patient advocacy, social media, the digitization of health, and the neoliberalization of academic research, a range of ethical frameworks inform the modes of participatory genomic research. Using illustrations from citizen genomic science, patient advocacy, and investigator-led and government-initiated genomic research efforts, we argue that as participatory genomic research pushes the conventional research boundaries toward a more democratizing ethos, it challenges scientific practices and the ethical conduct of genomic research both within and outside of the traditional sites of biomedical innovation.
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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 | 0.087 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.005 | 0.048 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".