Bibliographic record
Abstract
There is more and more interest in different forms of knowledge creation and management, and the conditions necessary to succeed in such initiatives from the point of view of individuals and organizations. A great deal of this interest stems from the fact that organizations expect substantial gains from knowledge. Knowledge management is seen in many organizations as a source of potential competitiveness and innovation. The concept of communities of practice stems from this interest, but is viewed as a specific form of knowledge development, 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 the organization. Organizational learning is part of a broader concern related to the development of collective skills. We know that a large proportion of effective relations within organizations are informal, a characteristic that relates to the concerns of the communities of practice, which are usually based on informal relations. Organizational learning goes beyond individual learning, which can lead to relatively permanent changes in the individual’s behavior, because it results in the development of a knowledge base which could translate into a more significant change of another kind within the organization. The knowledge is disseminated throughout the organization, is transmissible between members, is subject to consensus, and is integrated into the work processes and the structures of the organization. From this perspective, organizational learning is closely linked with “meaningful” organizational processes, which are basically routines used by decision makers to detect certain problems, define priorities, find solutions, and attempt to improve performance. In this article, we will present research results on some strategic objectives of CoPs and the attainment of these objectives, from the viewpoint of organizational learning.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".