Bibliographic record
Abstract
I argue that a Kantian inspired investigation into animal morality is both a plausible and coherent research program. To show that such an investigation is possible, I argue that philosophers, such as Korsgaard, who argue that reason demarcates nonhuman animals from the domain of moral beings, are equivocating in their use of the term ‘rationality.’ Kant certainly regards rationality as necessary for moral responsibility from a practical standpoint, but his distinction between the noumenal and phenomenal means that he can only establish it as a marker for morality from a theoretical standpoint. This means that when it comes to evaluating the moral capabilities of others, rationality can be neither necessary nor sufficient for morality, leaving open the possibility of other empirical markers for moral responsibility. I argue that the higher faculties, character, implicit knowledge of universality, and antecedent practical pleasures (which provide a way to distinguish between morally motivated behaviour and other types of socially motivated behaviour) can all serve as empirical markers for morality. There is empirical evidence that at least some animals have conceptual capabilities and therefore the empirical marker of the higher faculties. In addition, there is suggestive evidence that merits further investigation for the other three markers. While this will not provide a definitive answer on whether animals are capable of acting morally, it will provide a Kantian outlook that can be used to evaluate empirical and philosophical work on animal morality.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.021 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".