Bibliographic record
Abstract
Animal cruelty is a complex issue to investigate and even more difficult to prosecute successfully. In striving to improve the welfare of all animals, veterinarians and animal protection officers from societies for the prevention of cruelty to animals (SPCAs) are increasingly forming partnerships and a dependency on each other to prevent unnecessary pain and suffering and to relieve distress in all animals. This article seeks to offer a mere glimpse of the vital role that veterinarians play in the enforcement activities of SPCAs across Canada. Whilst my own experience is limited to the situation prevailing in Ontario, I will attempt to focus on a few topics that find application in all provincial societies and, I hope, amongst all veterinarians. Before embarking on such a discussion, it may be prudent to provide a brief synopsis of animal welfare legislation in Canada and to contrast the situation that exists federally, as opposed to provincially. The Criminal Code of Canada, which is federal legislation, applies in every province and territory, irrespective of whether there is a provincial/territorial animal welfare Act or not. Contained within this Code are a small number of criminal offences that, for want of a better term, I will title “Crimes against animals.” They are contained primarily in sections 444 through 447 of the Criminal Code. All provinces, with the exception of Quebec, have an animal welfare Act of some sort, which usually includes the establishment of provincial SPCAs. A number of these provincial animal welfare Acts contain provincial offences that prohibit the causing of distress in animals. In the true sense, these offences are not criminal matters, but rather are quasi-criminal in nature. Other provincial Acts have no provincial offences, and animal protection officers must rely solely on the Criminal Code of Canada to prosecute suspected cases of animal cruelty. All veterinarians should be familiar with the animal cruelty sections of the Criminal Code of Canada and with their respective provincial animal welfare Act.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.015 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.024 | 0.008 |
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".