The great cat mutilation: sex, social movements and the utilitarian calculus in 1970s New York City
Bibliographic record
Abstract
Abstract In 1976, the animal liberation movement made experiments conducted on cats at the American Museum of Natural History (AMNH) one of its earliest successful targets. Although the scientific consensus was that Aronson was not particularly cruel or abusive, the AMNH was selected due to the visibility of the institution, the pet-like status of the animals, and the seeming perversity of studying non-human sexuality. I contextualize the controversy in terms of the changing meaning of utilitarian ethics in justifying animal experimentation. The redefinition of ‘surgeries’ as ‘mutilations’ reflected an encounter between the behavioural sciences and social movements. One of the aims of the late 1960s civil rights movements was to heighten Americans’ sensitivity to differing experiences of suffering. The AMNH protesters drew inspiration from a revived utilitarian ethics of universal organismic pain across the lines of species. This episode was also emblematic of the emergence of an anti-statist, neo-liberal ethos in science. Invoking the rhetoric of the 1970s tax revolt, animal liberationists attacked Aronson's ability to conduct basic research with no immediate biomedical application. Without denying the violence involved, an exclusive focus on reading the experiments through the lens of utilitarianism obscures what ethics animated Aronson's research.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".