Toward a harmonized approach to animal welfare law in\nCanada
Bibliographic record
Abstract
Animal protection law in Canada varies across the country. Federal animal protection\nlaw exists in the Criminal Code, in regulations for the transport of\nanimals, and in regulations for humane handling and slaughter at abattoirs that are\ninspected by the Canadian Food Inspection Agency. Provincial animal protection laws\noften include provisions that i) describe a duty of care toward animals; ii) prohibit\ncausing or permitting animal “distress;” iii) specify exemptions from\nprosecution; and iv) reference various national and other standards. Inconsistencies\nlead to duplication of effort, create difficulty in working across jurisdictions, and\nmay erode public trust. A more consistent approach might be achieved by i)\nreferencing a common suite of standards in provincial statutes; ii) citing the\nfederal transport and humane slaughter regulations in provincial regulations; iii)\nestablishing agreements so provincial authorities may enforce federal regulations;\niv) wider and more uniform adoption of enforcement tools that require people to take\nimmediate action to protect animal welfare; v) developing new standards; and vi)\nnational consultation to define frequently used terms.
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 | 0.077 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.020 | 0.026 |
| Scholarly communication | 0.032 | 0.008 |
| Open science | 0.011 | 0.011 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 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".