Bibliographic record
Abstract
Robert Nozick’s oft-quoted review of Tom Regan’s The Case for Animal Rights levels a range of challenges to Regan’s philosophy. Many commentators have focussed on Nozick’s putative defence of speciesism, but this has led to them overlooking other aspects of the critique. In this paper, I draw attention to two. First is Nozick’s criticism of Regan’s political theory, which is best understood relative to Nozick’s libertarianism. Nozick’s challenge invites the possibility of a libertarian account of animal rights – which is not as oxymoronic as it may first sound. Second is Nozick’s criticism of Regan’s axiological theory, which is best understood relative to Nozick’s own axiological inegalitarianism. While Nozick’s axiology has distasteful consequences, it should not be dismissed out-of-hand. Nozick’s challenges to Regan – and Nozick’s wider animal ethics – are rich and original, warranting attention from contemporary theorists for reasons beyond mere historical interest.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".