Legal History and Rights for Nonhuman Animals:\nAn Interview with Steven M. Wise
Bibliographic record
Abstract
This article offers a window into the recent work of the Nonhuman Rights Project (NhRP) and its quest to secure legal personhood for cognitively advanced nonhuman animals (chimpanzees, elephants, and orcas). Law& History Professor Angela Fernandez interviews Nonhuman Rights Project founder Steven Wise about the work of his organization, setting the litigation strategy of the NonHuman Rights Project against the background of Wise's historical work on the 1772 British case that ended slavery in England, Somerset v. Stewart. The conversation Fernandez has with Wise ranges across the most recent decisions of the Nonhuman Rights Project cases, what has happened since the making of the documentary about the group's New York chimpanzee litigation Unlocking the Cage (2016), and reflections on issues that will be of interest to animal law lawyers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".