Animalization and dehumanization concerns: Another psychological barrier to animal law reform
Bibliographic record
Abstract
Legal systems across the world classify animals as property. There is growing global momentum asking courts in anthropocentric legal systems to revisit this position through test-case litigation. This has resulted in a few discrete victories for animals, but not much more. An ongoing issue is general legal conservatism and the belief in human exceptionalism that judges exhibit in these and related cases. In addition to general human exceptionalism, this article argues that a further psychological block for judges can arise from concerns about exacerbating racism and other intra-human prejudices given histories and legacies of animalizing and dehumanizing certain human groups. The first aim of this study is to illustrate this psychological phenomenon impacting judicial decision-making in relation to race. The article discusses the 2022 decision by the New York Court of Appeals with respect to the ongoing captivity of Happy, an elephant at the Bronx Zoo. This decision is selected given its recent and landmark status in North America. The second aim of the study is to outline why the dissociation of humans from animals is counterproductive to eliminating racism and other intra-human prejudices and inequities. The third aim of the study is to explain why affirming human proximity and kinship to animals—and thus putting a positive spin on animalization—in the legal system would be a more effective anti-racist and decolonizing gesture.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.042 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".