Bibliographic record
Abstract
The growth of virtual communities and their continuous impact on social, economic and technological structures of societies has attracted a great deal of interest among researchers, systems designers and policy makers to examine the formation, development, sustainability and utility of these communities. Over the last two decades, the growth in research into virtual communities, though fairly diverse, can be broadly categorized into two dominant perspectives—technological determinism and social constructivism. The basic tenet of the technology determinism research is that technology shapes cultural values, social structure, and knowledge. This Chapter provides a general overview of research on virtual communities. It describes two particular types of virtual communities relevant to the analysis of social capital described in the book; virtual learning communities and distributed communities of practice. The goal of the Chapter is to provide an overall context in which social capital is reported in the book. The Chapter also describes other areas in which virtual communities are currently used. These include education, health care, business, socialization and mediating interaction among people in Diaspora.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.091 | 0.031 |
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".