Menaces to Society: A Posthhumanist Rethinking of Canine Capital Punishment in Ontario
Bibliographic record
Abstract
This article presents a critical analysis of Ontario’s Dog Owners’ Liability Act (DOLA), focusing on its ethical and legal shortcomings. First, it highlights that DOLA permits courts to order the destruction of dogs deemed dangerous, a practice compared to capital punishment — which is something that has been abolished for humans in Canada. Second, it contends that dogs are often punished for actions that stem from human negligence, lack of training, or provocation, yet receive no legal representation or procedural fairness. Third, the critique underscores the speciesism inherent in the law, which treats dogs as property rather than as sentient beings. Finally, it proposes reform through alternatives such as provincially funded rehabilitation sanctuaries, aiming to promote a more compassionate and just legal framework.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.028 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".