Bibliographic record
Abstract
Communities of practice have been presented as the panacea of organizational learning. Building up on three case studies in different organizations characterized by different internal contexts, this article pushes the logic one step further by arguing that communities of practice can also be unique collaboration spaces within bureaucracies. Their main property is the ambiguity of their relationship with organizational control mechanisms and structures. Communities play with the rules, they can be adaptable and as such can build resilience within the organization. But this ambiguity, being the foundation of their capacity to introduce cooperation within organizations, is also difficult to maintain. Cultivating communities of practice thus becomes a delicate task for managers who must be able to adopt complex and contradictory behaviours. Five roles that can be fulfilled by management are analysed: stimulation, facilitation, support, control and recognition. Far from the generic recommendations that can be found in the literature to date, the findings indicate that the degree of intervention from management is highly dependent on the internal organizational context. This article thus provides a contingent framework to the cultivation of communities of practice.
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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.019 | 0.017 |
| Insufficient payload (model declined to judge) | 0.027 | 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".