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Record W7036631957

Can Sentience Recognition Protect Animals? Lessons From Québec's Animal Law Reform

2021· article· en· W7036631957 on OpenAlexaboutno aff

Bibliographic record

VenueeYLS (Yale Law School) · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMediterranean and Iberian flora and fauna
Canadian institutionsnot available
Fundersnot available
KeywordsSentienceAnimal welfareNormativeAnimal ethicsJurisdiction
DOInot available

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.712
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.037
GPT teacher head0.239
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

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