Improving Social Connection Through a Communities-of-Practice-Inspired Cognitive Work Analysis Approach
Bibliographic record
Abstract
OBJECTIVE: Increasingly, people work in socially networked environments. With growing adoption of enterprise social network technologies, supporting effective social community is becoming an important factor in organizational success. BACKGROUND: Relatively few human factors methods have been applied to social connection in communities. Although team methods provide a contribution, they do not suit design for communities. Wenger's community of practice concept, combined with cognitive work analysis, provided one way of designing for community. METHOD: We used a cognitive work analysis approach modified with principles for supporting communities of practice to generate a new website design. Over several months, the community using the site was studied to examine their degree of social connectedness and communication levels. RESULTS: Social network analysis and communications analysis, conducted at three different intervals, showed increases in connections between people and between people and organizations, as well as increased communication following the launch of the new design. CONCLUSION: In this work, we suggest that human factors approaches can be effective in social environments, when applied considering social community principles. APPLICATION: This work has implications for the development of new human factors methods as well as the design of interfaces for sociotechnical systems that have community building requirements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".