First impressions: the experiences of a community member on a research ethics committee.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it