Protecting Canada’s Lab Animals: The Need for Legislation
Bibliographic record
Abstract
Canada's current non-legislated oversight system for animal-based science not only fails to adequately incentivize the replacement of sentient animals as best scientific practice in any meaningful way, but also fails to adequately protect those animals bred, harmed, and killed in the name of science. In this paper, we outline the various shortcomings of the Canadian Council on Animal Care, and we highlight the need for Canada to move towards national legislation akin to that seen in other jurisdictions like the U.K. We conclude that while legislation alone cannot ensure the replacement of sentient animals in science, it appears to be a precondition for significant progress in animal protection and for the development and adoption of non-animal methods.
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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 | 0.047 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.022 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.013 | 0.020 |
| Insufficient payload (model declined to judge) | 0.007 | 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".