Bibliographic record
Abstract
This article explores the relationship between the rule of law and the situation of nonhuman animals. A commonplace view prevails that the rule of law in anthropocentric legal systems is unrelated to how we treat animals. In those rare instances when jurists have framed the legal treatment of animals as a rule of law problem, the connection has been a limited one (i.e., the rule of law is said to be violated when governments fail to enforce existing laws for animals’ benefit). This article presses the connection between the rule of law and animal justice beyond the issue of poor enforcement of anticruelty laws to build upon nascent scholarship theorizing legal systemic animal use as presenting a constitutional problem implicating the rule of law. The article asks whether Canadian jurisprudence contains precedent for a “thicker” vision of the rule of law that can incorporate animal interests in its purview to generate a higher standard of animal protection than the very little that currently exists. The article concludes that it does. Although the “thinner” version is the one that has been more frequently articulated by the Supreme Court of Canada, the analysis charts the significant precedent for a substantive vision, arguing that such a vision could theoretically extend to animals and that this doctrinal opening should not be summarily closed by ongoing anthropocentric reasons. The article further highlights existing legal commitments outside of conventional rule of law doctrine, namely reconciliation with Indigenous legal orders and adherence to customary international environmental law and developments in transnational environmental litigation, as additional doctrinal grounds as to why Canadian legal conversations and reasoning about what the rule of law means and protects should consider an animal-inclusive vision.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.031 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.033 | 0.007 |
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".