Bibliographic record
Abstract
Many commentators have argued that on Hume’s account, pride turns out to be something that is unstable, context-dependent, and highly contingent. On their readings, whether or not an agent develops pride depends heavily on factors beyond her control, such as whether or not her house, which is beautiful, is also the most beautiful in her neighborhood and whether or not her neighbors will admire the beauty of her house rather than become envious of it. These aspects of Hume’s theory of pride, the peculiarity requirement and the social dependency of pride, stand in tension with Hume’s claims that virtue reliably produces pride-in-virtue and that pride-in-virtue serves as a powerful motive to virtue. If pride depends on the affirmation of others and arises only from qualities that are peculiar to their possessor, will the virtuous person reliably develop pride-in-virtue? And if not, can pride-in-virtue serve the motivational role Hume attributes to it? This paper tackles these problems by showing how the virtuous develop pride-in-virtue and how the desire for pride-in-virtue can serve as a powerful and admirable motive to virtue.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".