Designing for Social Engagement in Online Social Networks Using Communities-of-Practice Theory and Cognitive Work Analysis
Bibliographic record
Abstract
New social networking and social web tools are becoming available and are easing the process of customizing online social environments for enterprises. With these developments, core design efforts have been extending beyond usability for individual users and are beginning to include notions of sociability for the engagement of communities of users. In an effort to make it easier to design for social engagement in an online social environment, the authors developed a domain-community model based on the communities-of-practice concept and the work domain analysis used in cognitive work analysis. Through a case study of University-Community Partnerships for Social Action Research, an international development leadership community of practice, the authors illustrate how the domain-community model could be used to design web components of an online social environment that integrate internal issues of social engagement and external issues of domain effectiveness. It is expected that this model can provide a basis for designers of online communities to more systematically account for social phenomena of collective efforts in a given work domain.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| 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".