Notice bibliographique
Résumé
Communities of practice have been in existence since the days when individual craftsmen got together to share ideas and issues. Eventually, these developed into craft guilds and finally into professional associations. But more specifically, focused communities of practice have recently begun to attract a great deal of attention in the business community because they provide a way for strategically growing and managing knowledge as an asset (Grant, 1996; Nonaka & Takeuchi, 1995; Powell, 1998). The increasing complexity in products, services, and processes requires more specialization and collaboration between workers. However, orchestrating the involvement of disparate groups that work on complex projects requires finding a balance between differentiation, when teams work separately, and integration, when groups meet to exchange knowledge. For example, development projects usually benefit when expertise is drawn from diverse sources, including potential users, where the interests, skills, and formal and tacit knowledge of the different groups can be drawn together by skillful project managers (Garrety, Robertson & Badham, 2004). By responding to new economic pressures for rapid transformation, communities of practice can help improve knowledge exchange in critical areas, so organizations can maintain or improve their competitive positions. The growth of interest in communities of practice has resulted in their spread into several classifications of modern organizations, all of which must share knowledge and learning to thrive. How effectively communities of practice perform in these different environments is of great interest, and, in order to study them in detail, we suggest classifying them according to the structure of the organizations they serve. We have been able to identity four such classifications: internal communities of practice, communities of practice in network organizations, formal networks of practice, and self-organizing networks of practice. Among these four classifications are characteristics of particular interest, especially when successful practices exhibited in one classification can be replicated in others. This article outlines the characteristics of each classification, explores their differences and similarities, and summarizes the findings from a review of the literature. The objective of this article is to encourage the migration of successful ideas for knowledge transfer and learning among the different classifications.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».