Bibliographic record
Abstract
Abstract.While confronting questions about the negative political effects of faction and fanaticism, David Hume developed a distinction between the manipulative rhetoric of the fanatics and the factional leaders and a good form of rhetoric that I term accurate, just, and polite. This high form of rhetoric combines Hume's philosophy of just reasoning with the rhetorical style of an idealized Demosthenes and eighteenth-century standards of politeness. Understanding Hume's conception of rhetoric is important for understanding the full scope of his political philosophy. In addition, further study of his conception of rhetoric could provide a valuable avenue of research for contemporary liberal theorists seeking to develop normative models of judgment and deliberation. Résumé.En réfléchissant aux effets négatifs du factionnalisme et du fanatatisme, David Hume a établi une distinction entre la rhétorique manipulatrice des leaders factionnaires et fanatiques, et une rhétorique que je qualifie ici de correcte, juste et polie. Cette dernière s'inspire de la philosophie du juste raisonnement de Hume, mais aussi d'un style de rhétorique associé à Demosthenes, érigé ici en idéal, ainsi que des standards de politesse du dix-huitième siècle. Cette conception de la rhétorique joue un rôle important dans la philosophie politique de Hume et pourrait constituer une avenue de recherche intéressante pour les penseurs libéraux contemporains qui cherchent à développer des modèles normatifs de jugement et de délibération.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".