Bibliographic record
Abstract
This is my first greeting as President and I look forward to my term as NCEHR continues with its mandate in research ethics. We are now an incorporated entity, continuing with our structure of council members reflecting the broad range of human research in Canada. Our last Council meeting was enhanced by the presence of a number of observers from not only Canadian organizations involved in research but also representation from the Office of Human Research Protections in the U.S. It is only by working together that we recognize gaps and overlaps. The last several months have been ones of intense activity. Our funding was received late in the fiscal year, allowing little time to accomplish our goals before the end of March and continue to plan into the next year. Site visits, one of our main activities, were resumed. These continue to be rewarding as we observe often innovative solutions and at the very least, thoughtful discussion around the review of research ethics. In analyzing the past three years of visits, we note recurring issues (particularly with those smaller or newer REBs), but increasing concerns with conflict of interest and privacy. Two site visitor training workshops were organized and now deemed a great success. The new surveyors will join the teams immediately in future site visits. In a joint venture with the Panel on Research Ethics, we have offered a new REB 101-type course covering social science and humanities issues. What is clear from these sessions (and the site visits) is a pressing need to enhance the Tri council Policy Statement to better reflect specific concerns of this broad community. In acknowledging this need, we have
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.278 | 0.159 |
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".