Bibliographic record
Abstract
Over the last decades, there has been more and more interest in various modes of networked learning, knowledge creation, communities or practice (CoPs) but there is not yet a clear identification of the conditions to succeed in such initiatives. This interest for CoPs stems from the fact that organizations expect substantial gains from knowledge development and networked learning. Communities of practice are seen in many organizations as a source of networked learning, and ultimately of competitiveness and innovation. The interest for communities of practice arises from this objective of learning and innovation, but it is viewed as a specific form of learning and sharing, in principle more centred on the individuals and their exchanges than on “management” by the firm, although the firm does seem to have a role to play in fostering such initiatives. Thus, the use of communities of practice has emerged as a way to develop collective skills and organizational learning, in order to foster innovation and success for organizations. In this paper, we identify the conditions of success or failure of communities of practice as a mode of networked learning, knowledge management and knowledge sharing, as these conditions have not yet been established. We first define this new form of learning and knowledge sharing through communities of practice. We then present some of the results concerning success, or more precisely attainment of objectives, as success can be defined in various ways. We do this on the basis of 7 case studies of communities of practice implemented in firms. The empirical results are based on a questionnaire survey administered to the participants of these communities of practice, but also on qualitative interviews and regular work and exchanges with some of the animators and participants in these communities of practice. We highlight some interesting differences observed according to age and gender, as well as some limits and challenges that were observed in the learning and sharing process, which are often underestimated. We mainly highlight the factors which explain success, defined as attainment of objectives, and these are : commitment and motivation of participants for the attainment of objectives, as well as the presence of a leader, animator or steward.
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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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.044 | 0.015 |
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".