Stepwise Imperatives for Improving the Protection of Animals in Research and Education in Canada
Bibliographic record
Abstract
In this paper, we review the standard-setting tools of different levels of government in Canada for overseeing the care of animals used in science against a landscape of other international efforts. We find regulatory inconsistencies, argue that the related shortcomings are detrimental to the level of care afforded to animals, and offer suggestions for a centralized and proactive approach that could close the existing gaps. Given the resources, cost, and time it would take to transform the current system into a single cohesive one, the proposed approach is a stepwise one, and begins with the addition of two new Rs—Reflection and Responsiveness—to the existing 3Rs framework: Replacement, Refinement, and Reduction. Reflection emphasizes more continuous and specific attention to progress in the research pathway as it applies to animals than is currently required by institutional review and reporting; Responsiveness speaks to the immediate action that researchers can take responsively to that ongoing evaluation.
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.
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 | 0.155 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.024 | 0.035 |
| Scholarly communication | 0.021 | 0.008 |
| Open science | 0.009 | 0.021 |
| Research integrity | 0.009 | 0.018 |
| 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".