Organisational Change Elements of Establishing, Facilitating, and Supporting CoPs
Bibliographic record
Abstract
Although knowledge management (KM) is often proposed as a viable means to enhance business performance by facilitating knowledge creation and sharing, there is serious concern that it frequently fails to deliver on its promise (Despres & Chauvel, 2000; Fuller, 2001; Newell, Scarbrough, Swan & Hislop, 1999; Pietersen, 2001; Brown & Duguid, 2000; Storey & Barnett, 2000). Smith and McLaughlin (2003) posit that KM’s lacklustre performance can often be traced to non-rational emotion-based “people-factors” that negatively influence interpersonal relationships, and that are ignored during typical KM implementation. These authors argue that the success of any significant change initiative, including KM, will be critically dependent on understanding, and improving as necessary, the collaborative characteristics of the organisation’s culture. This article adopts the notion that effective KM is largely people-centric, and that communities of practice (CoPs), when suitably grounded, provide a practical framework for assisting in the development of appropriate “people-factors” and the nurturing of collaborative relationships. It builds on the work of Smith and McLaughlin (2003) by proposing an extension of their approach that helps ensure the presence of a truly collaborative culture in the target community.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".