Analysis of processes of cooperation and knowledge sharing in a community of practice with a diversity of actors
Bibliographic record
Abstract
According to some literature, communities of practice should normally stem from a voluntary initiative within an organization, whose members share some knowledge or expertise they wish to improve. However, over time, we have seen that communities tend to be created within organizations, in order to attain objectives of learning and knowledge development. This represents a challenge in the context of a community of practice taking the form of a research network in partnership that brings together members with common interests certainly, but spread out in different organizations and even several countries in which they perform different types of work. Also, the community does not exist in a vacuum and the explanation for what happens within it does not lie solely within the way the group interacts; indeed the individuals are part of different organizations and thus have different priorities, in relation with these affiliations. In this context, our research objective was to determine the factors that facilitate or hinder cooperation within a community of practice composed by two groups of actors, community and university actors. We thus found that individuals? different work affiliations might not facilitate the work within the CoP and that ICT/web 2.0 tools are not always a solution to increase participation in a CoP. Although participants are somewhat familiar with the tools, they mostly seem content with receiving and accessing information, not searching for a more active participation. Some explications and solutions will be proposed.
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 | 0.020 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".