La conception du raisonnement de John Broome: «Que nous exprimons-nous lorsque nous raisonnons?»
Bibliographic record
Abstract
ABSTRACT This article offers a critical analysis of John Broome's conception of practical reasoning. I first introduce his main claims and then point out some of the difficulties raised by the notion of “double expression” and by some aspects of the cognitivism which he explicitly endorses. I then emphasize two consequences of these criticisms: one concerning the link he sees between belief and intention, and the other concerning the idea that our practical reasonings are inextricably linked to our theoretical reasonings. Finally, I argue that the problem Broome seems to be facing has its source in the difficulty of distinguishing clearly between reasoning (a relation between mental states) and the description of its content (a relation between propositions).
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.046 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".