La co-construction des savoirs au prisme de l’épistémologie et des inégalités sociales
Bibliographic record
Abstract
À la lumière du programme de recherche sur les savoirs et les inégalités sociales dans le champ de la santé et des services sociaux que je poursuis dans un centre de recherche montréalais, cet article présente une réflexion critique sur le concept de « co-construction » des savoirs. Il aborde quatre défis épistémologiques et politiques auxquels les universitaires en sciences sociales devraient prêter une attention particulière. Malgré ces défis, la co-construction des connaissances apparait comme une pratique de recherche éminemment subversive vis-à-vis des hiérarchies qui existent entre les savoirs et constitue un horizon nécessaire de ma pratique sociologique.
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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 | 0.033 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.013 | 0.085 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".