Bibliographic record
Abstract
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.
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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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".