Breaking barriers to ethical research: An analysis of the effectiveness of nonhuman animal research approval in Canada
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
as a guiding ethical framework. To ensure these standards are strictly enforced, internal ethics committees at each institution are tasked with creating "Animal Use Protocol" (AUP) forms to be filled out by researchers and evaluated by the committees.In this paper, we assess AUP forms from Canada's top research universities to identify the extent to which they conform to, or advance, the 3Rs framework. Our results show various deficiencies that call into question the quality of information elicited by these forms. To remedy this, we recommend that the CCAC assume responsibility for creating a standardized 3Rs section to be used on all AUP forms. In addition, proposal forms and experimental results for all research at CCAC-certified institutions should be digitized and uploaded into a national database. We argue that this would offer higher quality information for researchers at the experimental design stage, while strengthening the CCAC's mandate to be accountable to the Canadian public.
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.
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Observational | high |
| gpt | Research integrityMetaresearch Domain: Methods · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Observational | low |
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 | 0.082 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| 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