Bibliographic record
Abstract
Immanuel Kant holds that rational agency is a necessary condition to merit direct moral consideration;[1] therefore, he claims that we have no direct duties to animals. Nevertheless, he argues that we still ought to treat animals well, but only because we have duties to protect and develop our own moral character. Thus, what appear to be duties to animals themselves are, according to Kant, only indirect duties to them. However, the substantial challenge here is figuring out whether Kant’s indirect duties can provide a clear and adequate scope of our moral obligations concerning animals. In this paper, I argue that it cannot: if animals matter morally only in relation to our moral development, then our obligations regarding animals would be too vague and inadequate. To make my argument, I will examine some of Kant’s normative claims regarding how we should treat animals, and then demonstrate that what may appear morally enhancing can or may morally desensitize us. By demonstrating that the causes of moral desensitization are not categorical, I will show that it is insufficient to place our moral development as the only basis for our concern regarding animals. Furthermore, Kant’s indirect duties are a corollary of his metaethical commitments; therefore, by revealing problems that result from his indirect duties, I will infer that his metaethics need to be revised. My task in this paper is not to revise Kant’s ethics concerning animals, but to prove that it requires revision.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 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".