Gestion des Connaissances dans le Secteur Public : Modèles d'Excellence à l'Échelle Internationale
Bibliographic record
Abstract
Résumé : Cet article analyse la transformation de la gestion des connaissances (GC) dans le secteur public à travers une étude comparative des pratiques adoptées dans des pays comme les États-Unis, le Canada, la Corée du Sud, le Royaume-Uni et l’Australie. Notre objectif est de comprendre comment les administrations publiques peuvent utiliser la GC pour relever les défis croissants de complexité administrative, tout en améliorant la qualité des services publics, la transparence et l’engagement citoyen. La question principale explorée porte sur l’exploitation des connaissances comme levier stratégique pour la modernisation du secteur public. Nous avons adopté une revue intégrative combinée à l’analyse d’études de cas empiriques. Ces cas montrent l’importance de la digitalisation, de l’innovation technologique et de la collaboration interinstitutionnelle. Nos conclusions s’appuient sur l’économie de la connaissance (Drucker, 1993), la théorie des capacités dynamiques (Teece et al., 1997), et l’apprentissage organisationnel (Nonaka & Takeuchi, 1995). Elles mettent en lumière les convergences sur l’importance du partage des connaissances, mais pointent des divergences quant aux outils institutionnels. Les principales limites identifiées concernent le manque d’adaptabilité universelle des modèles aux contextes bureaucratiques et l’évaluation des impacts concrets sur les performances organisationnelles. Les mots clés : Gestion des connaissances (GC)- Secteur public- Digitalisation- Innovation technologique-Efficacité administrative- Partage des connaissances- Engagement citoyen- Transparence-Gestion stratégique des connaissances.Haut du formulaire JEL Classification : O32 Type du papier : Recherche théorique Abstract: This paper examines the transformation of knowledge management (KM) in the public sector through a comparative analysis of practices and strategies adopted across various countries. Drawing on case studies from the United States, Canada, South Korea, the United Kingdom, and Australia, the article highlights how digitization, technological innovation, and collaboration have enhanced administrative efficiency and the quality of public services. Each case study provides unique insights into how public administrations can leverage KM to address modern challenges by promoting knowledge sharing, transparency, and citizen engagement. Benchmarking reveals diverse approaches and promising outcomes, demonstrating that strategic knowledge management is a key driver for modernizing the public sector globally. Keywords: Knowledge Management (KM) - Public Sector - Digitization - Technological InnovationAdministrative Efficiency - Knowledge Sharing - Citizen Engagement - Transparency - Strategic Knowledge Management. Classification JEL: O32 Paper type: Theoretical Research
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".