The Priority of Suffering Over Life. How to Accommodate Animal Welfare and Religious Slaughter
Bibliographic record
Abstract
Most contemporary Western laws regarding the treatment of animals in livestock farming and animal slaughter are primarily concerned with the principle that animal suffering during slaughter should be minimized, but that animal life may be taken for legitimate human purposes. This principle seems to be widely shared, intuitively appealing and capable of striking a good compromise between competing interests. But is this principle consistent? And how can it be normatively grounded? In this paper I discuss critically this principle (the priority of the minimization of animal suffering over animals’ right to life). I argue that this principle can be justified on the ground of respect for the value commitment toward animal welfare, which is held by many people. The advantage of this perspective is its inclusiveness: it can justify without contradiction the principle at stake and allow for the admissibility of religious slaughter while promoting animals’ interest in not suffering. This justification also has the advantage of being compatible with the cultural and religious pluralism of contemporary societies.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.047 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".