Bibliographic record
Abstract
Ce papier présente un cadre logique capable d'exprimer un contextualisme épistémologique.Le contextualisme épistémologique repose sur la possibilité d'une interprétation indexicale des connaissances de l'opérateur, selon laquelle les conditions de vérité des attributions des connaissances manifeste une variabilité contextuelle d'une telle manière que les connaissances dans un contexte n'implique pas de connaissances dans d'autres contextes.Au moyen d'une notion de contexte épistémique définie sur la base de la notion de contexte développée par McCarthy et Buvac en Intelligence Artificielle, le papier montre comment une interprétation indexicale des connaissances de l'opérateur peut être modélisée formellement à travers un système de déduction naturellequi permet le raisonnement classique parmi des contextes gouvernés par différents concepts de connaissance.ABSTRACT.This paper aims at presenting a logical framework capable of expressing epistemological contextualism.Epistemological contextualism relies upon the possibility of an indexical interpretation of the knowledge operator, according to which the truth conditions of knowledge attributions exhibit a contextual variability in such a way that knowledge in one context does not entail knowledge in every context.By means of a notion of epistemic context defined on the basis of the notion of context developed by McCarthy and Buvač in artificial intelligence, I show how an indexical interpretation of the knowledge operator can be formally modeled through a natural deduction system that enables classical reasoning among contexts governed by different concepts of knowledge. MOTS-CLÉS.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".