John Dewey et sa glose approfondie de la théorie peircienne de la qualité
Bibliographic record
Abstract
Dans un article peu connu sur la doctrine peircienne de la qualité, John Dewey a non seulement précisé la nature et le rôle du qualitatif dans le système des catégories, mais il a également mis en évidence les implications philosophiques et méthodologiques de cette découverte fondamentale. Il s’est attaché à développer cette doctrine et à en chercher les applications dans d’autres écrits essentiels, où la catégorie de la qualité se trouve associée à la production de formes proprement iconiques et à la constitution de formes de l’expérience moins artistiques qu’esthétiques. J’essaie dans cet article de retrouver les racines de la pensée de Dewey dans l’oeuvre de Peirce et de dégager non seulement les rapports qu’entretiennent les deux projets philosophiques, mais aussi leurs capacités heuristiques respectives.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".