Canadian Librarians as Research Ethics Board Members: An Exploratory Case Study
Bibliographic record
Abstract
Very little is known about Canadian librarians who sit on Research Ethics Boards (REBs) or Institutional Review Boards (IRBs). There is some background information on various roles of librarians on REBs, but that is usually from the perspective of one or two authors and often relating to the health sciences. Librarianship continues to evolve, especially in the areas of research data management, research support, and scholarly communication. It is, therefore, important to understand the changing expectations and responsibilities that librarians may have in Canada in order to encourage and support them as they meet institutional needs. This exploratory case study will document the reasons that motivate Canadian librarians to sit on REBs, the training they have received, the benefits achieved, and their overall experience.
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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 | 0.020 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.052 | 0.012 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".