défi de vigilance méthodologique en recherche collaborative : des ficelles pour négocier des tensions dans des activités de co-analyse de savoirs avec des enseignants migrants
Bibliographic record
Abstract
Comment s’y prendre lorsque la prise en charge des demandes des participants risque de compromettre la cohérence avec les exigences de reconnaissance des expertises de chacun et de symétrisation des positions au coeur même de la recherche collaborative ? En réponse à cette question, cette contribution documente six « ficelles méthodologiques » mobilisées dans une démarche de vigilance méthodologique prenant en compte les rapports de pouvoir symboliques entre chercheur et praticiens. Globalement, ces ficelles ont permis de négocier des tensions – méthodologique et épistémologique – qui ont émergé dans les activités de co-analyse de savoir-évaluer avec des enseignants migrants au Québec.
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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 | 0.177 | 0.175 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.017 | 0.080 |
| Scholarly communication | 0.028 | 0.020 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.007 | 0.009 |
| 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".