Organisational Change Elements of Establishing, Facilitating, and Supporting CoPs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 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 it