A Critical Assessment of Bill S-203, Ending the Captivity of Whales and Dolphins Act: Challenging the Exclusivity of Anthropocentrism and Science-Based Justifications
Bibliographic record
Abstract
Bill S-203, An Act to amend the Criminal Code and other Acts (ending the captivity of whales and dolphins) became Canadian law in 2019, banning the captivity of cetaceans. This Article critically examines Bill S-203, arguing that it is underpinned by anthropocentric and science-based justifications that will work as exclusionary forces against many animals in need of legal protection. Instead, the Article advocates for an empathetic and multi-jural approach that accounts for human-animal interconnectedness and the unique cultures of animals. This argument is theoretically rooted in vegan ecofeminism’s empathic and non-binaristic perspective. As such, this Article scrutinizes the reasoning behind Bill S-203, asserting that the justifications employed by its proponents are exclusionary. The bill was presented through an anthropocentric lens, focusing on minimizing the captivity of select humanized animals while overlooking other unique animal qualities. Additionally, similar proposed legislation for great apes and elephants would perpetuate these anthropocentric tendencies. An alternative, multi-jural approach to legal reform that rejects anthropocentrism and science-based reasoning can recognize animal-human interconnectedness by leveraging the language of Indigenous legal orders in Canada. Such an approach would acknowledge the distinct norms, lifestyles, and cultures of animals. This Article contributes to existing literature by emphasizing empathy, alterity, and the importance of recognizing the interconnectedness of all life forms. It strives to carve out a space for the legal consideration of the ‘laws’ of other-than-human animals, challenging prevailing anthropocentric paradigms in animal legal protections.
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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 | 0.038 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.026 | 0.045 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.027 | 0.030 |
| Insufficient payload (model declined to judge) | 0.002 | 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".