The Role of an Ethics Committee in Co-Produced Research: The Experience of The Disability Research on Independent Living and Learning (Drill) Project
Bibliographic record
Abstract
Abstract This chapter explores the role of an ethics committee led by disabled academics, in supporting co-produced disability research beyond academia, in the context of a five-year research programme in the UK (2015–2020). This chapter includes reflections by the Ethics Committee members, alongside documentary research which analysed the communications between the Ethics Committee and the research projects it supported. This review of the role of the Ethics Committee showed that there were dilemmas in considering the boundaries between ethical review and providing pedagogic advice on research design, and in balancing its role in supporting and regulating research. Ethics review processes are sometimes seen as overly bureaucratic and as an obstacle course for researchers, and this was also sometimes the case for projects supported by the DRILL (Disability Research on Independent Living and Learning) Ethics Committee. Lessons to be learned from the process included that communication between ethics committees and researchers is key, and that ethics review can be a two-way process, recognising the expertise of both the researchers and the reviewers, thus mirroring the principles of co-production. We suggest that an alternative model for ethics review process could build on this generally positive experience of the DRILL Ethics Committee.
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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 | 0.105 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.026 | 0.046 |
| Scholarly communication | 0.026 | 0.016 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.007 | 0.019 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".