Can Sentience Recognition Protect Animals? Lessons From Québec's Animal Law Reform
Bibliographic record
Abstract
Academic literature needs to provide a better understanding of the legal recognition of animal sentience. This Article aims to help fill out this gap by diving into Que ́bec’s legal recognition of animal sentience in 2015. This Article draws three lessons from Que ́bec law’s recognition of animal sentience and biological needs. First, it argues that legal sentience recognition’s fate is to become more than symbolic and to receive normative force. Second, it contends that considering sentience protection as the sole instrument to prevent animal killing and exploitation is a mistake. This is so because respect for sentience is reduced to suffering prevention by judges. As such, sentience protection in Que ́bec simply hardens the requirements for painlessness in animal killing and exploitation but does not decrease the practice. Third, it suggests that the legal protection of “biological needs” might bring hope where sentience does not. This alternative concept tackles some of sentience’s blind spots. This Article is of interest to any lawyer, academic, and activist operating in a jurisdiction where the law explicitly declares that animals are sentient beings and to those in other jurisdictions that are considering recognizing animal sentience by statute.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".