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Record W132244692

Human Drama, Animal Trials: What the Medieval Animal Trials Can Teach Us About Justice for Animals

2011· article· en· W132244692 on OpenAlexaffabout
Katie Sykes

Bibliographic record

VenueSSRN Electronic Journal · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsThompson Rivers UniversityDalhousie University
Fundersnot available
KeywordsEconomic JusticeLawPolitical scienceAnimal rightsEmpathyLegal historyPsychologySocial psychology
DOInot available

Abstract

fetched live from OpenAlex

The legal system generally does little to protect animals, and one aspect of its inadequacy is a matter of formal structure: under United States and Canadian law, animals are not legal “persons” with an independent right to the protections of the legal system. There are calls to expand the status of animals in the law by providing them with legal standing, the right to be represented by a lawyer, and other formal protections. But, in a way, some of this has happened before. There is a long history, primarily from the medieval and early modern periods, of animals being tried for offenses such as attacking humans and destroying crops. These animals were formally prosecuted in elaborate trials that included counsel to represent their interests. The history of the animal trials demonstrates how, in a human-created legal system, legal “rights” for animals can be used for human purposes that have little to do with the interests of the animals. This history shows us that formal legal rights for animals are only tools, rather than an end in themselves, and highlights the importance not just of expanding formal protections, but of putting them to work with empathy, in a way that strives (despite the inevitable limitations of a human justice system in this respect) to incorporate the animals’ own interests and own point of view.

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)0.050
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.680
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0500.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0050.001
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.109
GPT teacher head0.404
Teacher spread0.294 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
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

Citations24
Published2011
Admission routes2
Has abstractyes

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