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Record W2092500444 · doi:10.1353/hms.2012.0011

The Ways of the Wise: Hume’s Rules of Causal Reasoning

2012· article· en· W2092500444 on OpenAlexvenueno aff
Deborah Boyle

Bibliographic record

VenueHume studies · 2012
Typearticle
Languageen
FieldArts and Humanities
TopicPhilosophical Ethics and Theory
Canadian institutionsnot available
Fundersnot available
KeywordsEpistemologyDoxastic logicSkepticismVirtueEpistemic virtueInferencePhilosophyCausal inferenceNormativeCausal reasoningPsychologyCognition

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.495
Threshold uncertainty score0.677

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.166
GPT teacher head0.302
Teacher spread0.136 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2012
Admission routes1
Has abstractyes

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