First impressions: the experiences of a community member on a research ethics committee.
Bibliographic record
Abstract
Jewish General Hospital suggested I join as a community representative. I thought it over and decided to give it a try. I sent a CV to the research ethics officer, had a brief interview with the committee chair, and a few short weeks later found myself approaching the hospital boardroom with my knees shaking, palms sweating, and heart pounding quite uncertainly in my chest. So began what has become a challenging, rewarding, and sometimes confusing acquaintance with the medical world. Before I stepped into that first meeting, I knew only two things for sure: a lot of reading was required, and they gave you lunch. After three or four meetings I learned two more things. First, the committee was obviously not counting on my contribution to the discussion of the scientific or medical aspects of the research, so my efforts would best be spent on the informed consent document. And second, the committee was made up of doctors and others representing various specialties with an interest in research: a pharmacist, a jurist, an ethicist, nursing representatives, the hospital's patient representative, and we three community representatives. I found that, as individuals, each was conscientious and approached his or her work on this committee in a serious and responsible
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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 | 0.059 | 0.162 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.065 | 0.024 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.014 | 0.028 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".