Beyond Sentience: Legally Recognizing Animals’ Sociability and Agency
Bibliographic record
Abstract
Abstract The recognition of animal sentience in law has created high expectations but has not yet lived up to them. In some jurisdictions, the recognition of animal sentience has formed the basis of new legal obligations imposed on humans to protect animal interests. So far, however, its potential has been limited because legal officials have interpreted sentience narrowly, as mainly referring to pain. This article proposes identifying other animal characteristics to better serve animal interests, namely sociability and agency. These animal characteristics could, like sentience, be legally recognized by legislatures and receive normative force from the courts. This article argues that the legal recognition of sociability and agency could address animal exploitation and killing. Animal sociability and agency are already implicitly recognized by the law of most Western states, making it easier for them to be explicitly recognized in the future. Although these characteristics are already found in the law, they have the potential to be more disruptive than sentience because they have never served as the basis for the legal protection of animals. In other words, their potential as protective tools remains underexplored and underexploited. Moreover, sociability and agency more intuitively suggest normative implications because of their demanding nature. Acknowledging that a being is social and agential implies recognizing her need for social relationships and ensuring that her choices are fulfilled. This research article provides antispeciesist scholars and activists with a critical overview of new concepts around which some may wish to rally, particularly since the legal recognition of sentience has shown limits in multiple jurisdictions. This article explores the potential of legally recognizing sociability and agency by taking examples from the lives of dogs, cows, cats, horses, northern gannets, and dolphins.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.074 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".