Parcours collectif d’apprentissage organisationnel :une stratégie de recherche qualitative porteusepour l’étude du transfert des connaissances
Bibliographic record
Abstract
Les experts en apprentissage organisationnel et en gestion des connaissances souhaitent une plus grande utilisation des méthodologies qualitatives de recherche pour comprendre les concepts liés à l’apprentissage et à la connaissance. L’objectif de cette communication consiste à proposer l’utilisation du learning history , une méthodologie de recherche qualitative créée par Kleiner et Roth (1995), pour l’étude du transfert de connaissances. Souvent utilisée dans un contexte de recherche-action, cette méthodologie est conçue pour permettre aux parties prenantes de reconnaître leurs apprentissages passés et pour les guider dans la génération dialogique de leurs actions futures. Cette communication survolera d’abord la littérature sur le learning history . Ensuite, nous démontrerons les avantages liés à l’utilisation de cette approche pour étudier le transfert de connaissances en présentant deux études de cas. Enfin, les leçons tirées de nos projets en cours nous permettront de proposer quelques implications pour la recherche et la pratique.
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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 | 0.083 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".