Measuring the value of online communities and networks of practice for business
Bibliographic record
Abstract
How are you measuring the value of your online communities and networks of practice? This research-in-progress paper identified the landscape of social networks, networked learning, and social network technologies. Based upon Wenger’s et al. (2011) framework on promoting and assessing value creation in communities and networks, a model has been identified to measure the success of the organization’s “knowledge collective” - the dialogue and learning in online communities and networks of practice in corporate environments. In our rapidly changing workplace landscape, augmenting formal performance improvement activities are informal learning and coaching incorporating social network technologies to increase involvement, strengthen relationships, and enhance individuals’ development. The methodology for the research study has been presented and used in conducting two concurrent studies in two countries (Netherlands and Canada). Preliminary results of the two studies will be presented at the conference and upon completion of the research, the results will be incorporated into this paper. The model presented in this study will provide an evidence-based instrument for organizations to measure the value of the dialogue in online communities to achieve business results.
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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 | 0.015 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".