Human Drama, Animal Trials: What the Medieval Animal Trials Can Teach Us About Justice for Animals
Bibliographic record
Abstract
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.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.108 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 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".