‘Animal Justice’ and Sexual (Ab)use: Consideration of Legal Recognition of Sentience for Animals in Canada
Bibliographic record
Abstract
With the recent developments surrounding R v DLW and the legal interpretation of ‘bestiality’ before the Supreme Court of Canada, animal law organizations such as Animal Justice insist that Canadians must recognize their obligation to protect the most vulnerable beings in their care, and not subject them to abuse. We argue that there were many avenues of interpretation open to the Supreme Court in adjudicating and addressing the legal definition of bestiality. The majority of the Supreme Court ultimately adopted a conservative approach to statutory interpretation. A strict legal construction and focus on original intent of Parliament foreclosed development of the law towards legal recognition of animal sentience and the concomitant implications for animal rights in Canadian law. In this paper we consider various routes by which a more progressive interpretation of bestiality could have been constructed by the Supreme Court of Canada. When the Supreme Court of Canada concluded that bestiality could only be interpreted as a penetrative offence, it avoided the chance for incremental legal change that could have contributed to the ways Canadians, laypersons, and legal professionals recognized animal consciousness. Animal protection and legal animal welfare apparati in Canada still remain relatively adrift, and less developed than in countries like New Zealand.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.023 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".