Développement professionnel de chercheurs découlant d’une recherche participative : le cas de l’École en réseau
Bibliographic record
Abstract
Cette Note du terrain propose une réflexion sur le développement professionnel au contenu imprévisible qui est survenu chez des chercheurs ayant mené une recherche participative pendant une quinzaine d’années. Pour nourrir la réflexion, nous avons travaillé à partir de quelques textes clés produits antérieurement, desquels nous avons identifié des traces de transformation de l’activité des chercheurs universitaires tout au long de trois grandes phases de la recherche. Ces traces ont ensuite été organisées à partir du modèle de la théorie de l’activité. La flexibilité dont nous avons fait preuve dans la conduite de la recherche, notamment pour tenir compte du contexte des participants, a engendré des apprentissages à différents niveaux.
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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 | 0.036 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.022 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".