Création et adoption de pratiques pour la conduite du changement : une démarche évolutive au sein d'une entreprise québécoise
Bibliographic record
Abstract
Les écrits, conférences et formations sur la conduite du changement sont fort nombreux. Pourtant plusieurs managers et conseillers responsables de la conduite du changement peinent à implanter des pratiques de conduite du changement au sein de leur entreprise. Afin de mieux comprendre cette dynamique, trois courants de recherche sont mobilisés : institutionnalisation, internalisation et maturité des pratiques en conduite du changement. Pour illustrer, une étude longitudinale sur quatre ans dans une entreprise québécoise relate les différentes étapes franchies afin d’internaliser la conduite du changement. L’analyse du terrain de recherche permet de formuler cinq recommandations pour les managers et les conseillers désirant instaurer des pratiques de conduite du changement.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".