La contribution de la gestion des connaissances à la gestion de la relève
Bibliographic record
Abstract
Résumé Deux enquêtes récentes mettent en évidence l’importance d’associer le domaine de la gestion des connaissances à l’enjeu de la gestion de la relève. Cet article illustre l’interaction de ces deux domaines à partir de l’étude de cas de la société Hydro-Québec. Après avoir situé le champ de la gestion des connaissances, nous présentons succinctement la stratégie de gestion de la relève d’Hydro-Québec en montrant en quoi différentes approches de gestion des connaissances contribuent à cette problématique organisationnelle. L’analyse du cas Hydro-Québec permet plus spécifiquement de montrer l’apport des communautés de pratique virtuelles aux enjeux stratégiques de gestion de la relève et du transfert des compétences. Enfin, l’article expose les facteurs clés de succès dans la mise en place de ces communautés et décrit la relation entre les domaines de la gestion des connaissances et de la gestion des ressources humaines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".