L’abduction et l’enquête sociale comme procédé méthodologique pragmatiste
Bibliographic record
Abstract
Dans cet article, nous poursuivons les efforts de réhabilitation de l’institutionnalisme pragmatiste de John Rodgers Commons, mais en portant cette fois notre attention que sur les aspects méthodologiques. À cet effet, nous nous intéresserons aux travaux de Charles Sanders Peirce et à sa méthode de l’« abduction » ainsi qu’à ceux de John Dewey et à son procédé d’« enquête », puisqu’ils ont inspiré Commons dans ses recherches. Nous situerons notamment ces démarches complémentaires en regard des procédés usuels de déduction et d’induction et comme voie alternative à la tradition analytique cartésienne. Nous discuterons du procédé actif d’expérimentation ou de mise à l’épreuve, la causalité circulaire, qui résulte de la mise en application de l’abduction et de l’enquête sociale. Enfin, nous rendrons compte de l’expérience d’enquête de notre thèse de doctorat, inspirée de la méthodologie pragmatiste.
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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 | 0.029 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.045 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".