Bibliographic record
Abstract
In their responses to Hume’s account of causal reasoning, Hume’s own contemporaries and many subsequent readers have tended to focus on the skeptical implications of that account. More recent scholarship has emphasized that Hume’s account of causal inference is not purely skeptical, for Hume often suggests that forming a belief through causal inference based on repeated experience is the right way to form beliefs. One less-noticed feature of Hume’s account of causal inference, however, is that Hume links good causal inference with virtue; thinkers who adopt certain methods of causal reasoning and eschew other methods possess the epistemic virtue that he characterizes as “wisdom” or “good sense.” This paper argues that Hume’s account of causal reasoning and his normative claims about belief can fruitfully be interpreted by focusing on what Hume says about such doxastic wisdom: why he thinks it is better to be wise than unwise; what he means when he characterizes certain methods of belief-formation as wise; how the cognitive habits employed by the wise differ from those of the unwise; and how he thinks someone can who lacks the epistemic virtue of wisdom can come to acquire it. Since much of the secondary literature on Humean virtue has focused on the “moral” rather than “intellectual” virtues (EPM App 4.2; SBN 313), attention to Humean doxastic wisdom also helps to provide a more complete picture of his account of 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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.026 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".