Knowledge Extraction and Sharing in External Communities of Practice
Bibliographic record
Abstract
Communities of practice (CoPs) may be described as groups whose members regularly engage in sharing and learning, based on common interests (Lesser & Storck, 2001). Traditional communities of practice exist within organizations and are centered on work functions. These CoPs may be self-organizing or corporately sponsored. They exist to encourage learning and interaction, create new knowledge, and identify and share best practices for the organization’s processes (Wenger, 1998). The members of a community of practice may be collocated (within an office) or spatially dispersed (e.g., a group may interact via electronic chat). There may also be communities of practice that are not centered on work functions. For example, several online groups exist for enthusiasts of new technology, politics, environment, and so forth. These groups qualify as bona fide CoPs. We classify the CoPs discussed so far as active communities of practice because the members actively seek to learn and share from each other. In this work, however, we examine passive communities of practice in which the members do not actively interact with each other. This class of CoPs shares the core characteristics of traditional communities of practice—the members can learn from each other, and the organization can gain useful knowledge capital and best practices. Our discussions will be based on user communities using cable-TV viewers as a case in point. In contrast to work-centered CoPs whose members share knowledge and learn how to perform their work tasks better, members of user-centered CoPs learn how to maximize the utility from the product/service of interest. In both cases, a learning organization can extract useful knowledge capital and best practices to improve its processes and products/services. In this work, we use the case of cable-TV viewers to show how useful knowledge can be learned and shared in passive user-centered communities of practice. Our techniques will be based on data mining and knowledge discovery, which are introduced in the subsection that follows.
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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 | 0.012 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".