The Canadian Agency for the Oversight of Research Involving Humans: A Reform Proposal
Bibliographic record
Abstract
In this paper, I propose the creation of a Canadian agency for the oversight of research involving humans. I describe first a series of significant problems with Canada's current system of oversight. I then argue for the creation of a national-level agency, covering all research involving humans, with three branches (policy and standards, education, and compliance). Of particular note, the proposed compliance branch consists of a number of independent national and regional Research Ethics Boards (i.e., REBs no longer reside within institutions). There is also an Audit Committee and a Non-compliance Committee (with supporting staff of auditors and compliance officers) to ensure compliance with the policies and standards set by the Policy and Standards Branch. Finally, I answer a series of "frequently asked questions" about the proposed agency design such as "What about 'local context'?" and "Why not have a system of accreditation of institutional REBs instead?" In sum, radical reform is needed and, in this paper, I present a proposal for such reform.
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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 | 0.098 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.022 | 0.024 |
| Scholarly communication | 0.020 | 0.009 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.038 | 0.022 |
| Insufficient payload (model declined to judge) | 0.006 | 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".