Bibliographic record
Abstract
Hume claims that moral assessments refer to character; it is character of which we morally approve and disapprove. This essay explores what Hume means by "character." Is it true that moral assessments refer to character, and should Hume think this given his other commitments in moral philosophy and moral psychology? I discuss two prominent themes—namely, Hume's views on moral responsibility; and Hume's comparison of moral feelings with feelings of love—to see what light these themes can shed on Hume's broader views about moral assessment. I argue that at least according to a traditional understanding of the term, character could not plausibly have a role to play in Hume's account of moral assessment, but that Hume's moral theory could require a conception of character different from this traditional one: a conception according to which character need not be the standard one that holds character to be consistent, stable, and well-integrated. In morally assessing others, we do not do so on the basis of their characters (at least in any robust sense of character), but on the basis of their motivational states. My account of Hume's theory of the responsibility, passions and the moral sentiments leaves intact the central Humean insights about the conditions for action and the arousal of the moral sentiment, suggesting what Hume could have said, both more plausibly and without undermining the key features of his moral psychology. And it also shows that Hume's moral theory has no need for a robust conception of character.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.036 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".