Bibliographic record
Abstract
For knowledge to create value in an organization, whether tacit or explicit, it must have the ability to be shared among employees. This intentional (or in some instances unintentional) flow of knowledge can become the driver for organizational learning. When examining knowledge sharing, it is important to consider the context in which the knowledge is developed, as the community in which the individual is learning can affect any knowledge that is created. Organizational learning is impacted by individuals, groups, and the organization as a whole, and how these three levels are linked by social processes (Crossan, Lane & White, 1999). However, it is very difficult to create the right social environment to produce optimum knowledge sharing and learning. Sharing knowledge is an ‘unnatural act’, and therefore firms must strive to create the right environment and means to assist employees in overcoming knowledge flow barriers (Ruppel & Harrington, 2001). Previous research has identified communities of practice as a hub for sharing knowledge within an organization (Brown & Duguid, 1991; Ellis, 1998; Hildreth & Kimble, 1999). The ability of a community of practice to create a friendly environment for individuals with similar interests and problems to discuss a common subject matter encourages the transfer and creation of new knowledge. Practitioners with similar work experiences tend to be drawn to communities, and from this a common purpose to share knowledge and experience arises (Wenger, 1998). Blackler (1995) argues that the creation and deployment of knowledge is inseparable from activity, and different contexts manifest in the form of knowledge boundaries. A community of practice can help individuals remove this boundary through the creation of a common context that links different experiential knowledge in an environment suited for knowledge exchange.
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 | 0.023 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".