Ethical arguments that support intentional animal killing
Bibliographic record
Abstract
Killing animals is a ubiquitous human activity consistent with our predatory and competitive ecological roles within the global food web. However, this reality does not automatically justify the moral permissibility of the various ways and reasons why humans kill animals – additional ethical arguments are required. Multiple ethical theories or frameworks provide guidance on this subject, and here we explore the permissibility of intentional animal killing within (1) consequentialism, (2) natural law or deontology, (3) religious ethics or divine command theory, (4) virtue ethics, (5) care ethics, (6) contractarianism or social contract theory, (7) ethical particularism, and (8) environmental ethics. These frameworks are most often used to argue that intentional animal killing is morally impermissible, bad, incorrect, or wrong, yet here we show that these same ethical frameworks can be used to argue that many forms of intentional animal killing are morally permissible, good, correct, or right. Each of these ethical frameworks support constrained positions where intentional animal killing is morally permissible in a variety of common contexts, and we further address and dispel typical ethical objections to this view. Given the demonstrably widespread and consistent ways that intentional animal killing can be ethically supported across multiple frameworks, we show that it is incorrect to label such killing as categorically unethical. We encourage deeper consideration of the many ethical arguments that support intentional animal killing and the contexts in which they apply.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.035 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".