Collaboration recherche-pratique autour de situations d’enseignement : comment les dissensus aident à mieux comprendre les pratiques enseignantes
Bibliographic record
Abstract
Dans les recherches collaboratives que nous menons, les situations d’enseignement jouent un rôle central dans la mesure où elles servent de base de discussion entre des personnes enseignantes et chercheures. Or, il arrive que lorsque les personnes chercheures amorcent la discussion en proposant une tâche, elle soit rejetée par les personnes enseignantes. Dans cet article, nous proposons d’étudier plus en profondeur ces cas de rejet. En envisageant la situation d’enseignement sous l’angle d’un objet frontière tel qu’il est entendu par Star et Griesemer (1989), nous analyserons des extraits issus de deux recherches collaboratives dans lesquels des personnes enseignantes rejettent des situations d’enseignement proposées par les chercheurs.
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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 | 0.134 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.031 | 0.064 |
| Scholarly communication | 0.028 | 0.033 |
| Open science | 0.006 | 0.032 |
| Research integrity | 0.012 | 0.014 |
| 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".