Bibliographic record
Abstract
Herein I engage with the very difficult question of whether the duty to aid (sometimes called a duty of assistance or a duty of beneficence) extends so far as to justify harming persons, perhaps even lethally, in order to protect wild animals. I argue that this question is not nearly as settled as our intuitions may suggest and that Shelly Kagan’s arguments on Defending Animals, contained in his book How to Count Animals, More or Less, provide a rich substrate in which to cultivate ideas on this subject (2019, pp. 248–279). My intuition is that killing a person, even one ‘guilty’ of trying to kill an animal for sport or leisure, is far beyond what a duty to aid can command, though admittedly I find my own intuition somewhat morally dumbfounding. I argue further that Tom Regan’s ‘worse-off principle’ may ease the ever-uncomfortable sense of moral dumbfounding by providing a surer foundation for the intuitive sense that we cannot ethically go so far as to threaten a person with lethal force in defense of nonhuman animals.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".