Processus en jeu dans des projets de recherche participative : pourquoi s’y intéresser ?
Bibliographic record
Abstract
En introduction au colloque, le professeur Bruno Bourassa a partagé son expérience relativement à la pertinence de s’intéresser aux processus en jeu en recherche participative. Sous la forme d’un entretien, l’article relève les idées, plus particulièrement en regard de quatre grandes phases de la recherche que sont la constitution de l’équipe de recherche, l'activation de la démarche en réponse aux questions de recherche, la création des conditions de réalisation et d'apprentissage ainsi que l'interprétation des données et la diffusion des résultats. Son propos met en lumière des défis rencontrés dans des recherches participatives, notamment la gestion des dynamiques de groupe et l'adaptation nécessaire aux changements, d’où l’importance d’une flexibilité de toute l’équipe de recherche. En conclusion, M. Bourassa encourage la pérennisation des recherches participatives qui permettent une meilleure compréhension et une transformation des pratiques professionnelles et qui contribuent à l’avancement des savoirs scientifiques.
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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 | 0.073 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.021 | 0.039 |
| Scholarly communication | 0.026 | 0.017 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".