Bibliographic record
Abstract
Social capital in virtual communities offers a useful conceptual and practical tool to help us gain insights into the way people interact with each other, share information and knowledge among themselves and work together. This book has synthesized and brought together a massive volume of current and past work on social capital in geographical or place-based communities. The results of the analysis helped to extend the theory of social capital to virtual communities. It has also provided basis for e researchers, policymakers and systems designers to explore social issues that are likely to have an impact on information and knowledge sharing. The book provides useful information for people concerned with how social capital may be used to answer key questions about its fundamental components, how to study and model it within the contexts of virtual learning communities and distributed communities of practice. The main thrust of this book is the ability to identify the critical components of social capital in virtual communities and the use of modelling techniques—Bayesian Belief Network to analysis of interactions of the components of social capital. The components identified in the book serve as important proxies for examination of how social capital will operate in virtual communities. It is hoped that this fresh conceptualization of social capital in virtual communities prepares scholars to engage in useful and productive discussions on how to hone the potentials of this theory. This Chapter summarises the key issues presented in the book and outlines important future directions for the discussion of social capital in virtual communities.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".