La recherche interventionnelle en santé : divers engagements dans la production collaborative de connaissances
Bibliographic record
Abstract
En prenant pour terrain d’enquête un domaine de recherche interdisciplinaire et collaboratif émergeant dans le secteur de la santé, les Recherches Interventionnelles en Santé des Populations (RISP), cette contribution se propose de considérer les diverses formes d’engagement dans la production de ce type de connaissances. Sont ainsi repérées quatre figures d’engagement (afficher, éprouver, persévérer et figer) qui rendent compte de modes de coordination plus ou moins maximalistes entre les acteurs de ces recherches, en lien avec différentes conceptions et pratiques de la diffusion et de la circulation des connaissances. L’enquête se base sur trois grands types de données : des observations ethnographiques de RISP ainsi que des congrès et réunions de groupes d’experts produisant des réflexions sur ce type de recherches, des analyses d’écrits (articles, rapports, lettres d’information…) sur les RISP et des entretiens (12) avec les principaux experts du domaine.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.070 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".