Virtual Communities of Practice: Explaining Different Effects in Two Organizational Contexts
Bibliographic record
Abstract
This paper presents results from a study on virtual communities of practice (CoPs) in Canada and highlights the main consequences of these new modes of communication, work organization, and knowledge creation through two case studies, 1 which are analyzed in detail. The two case studies reveal the different effects observed and offer possible explanations for the variations in the results. Several factors explain the success of one community of practice and the relative difficulties involved in the other. In the first case, the participants were all volunteers and showed high levels of engagement and motivation to attain their objectives. In the second case, the participants were appointed and their project was somewhat more diffuse; the CoP experienced a turnover in leadership, contributing to a lesser degree of motivation and little interest in communicating with each other as a means of creating knowledge. This analysis contributes to organizational literature by highlighting some organizational conditions that lead to different effects in “virtual,” or telecommuting, communities of practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".