Bibliographic record
Abstract
Despite lack of meticulousness, social capital continues to occupy a central position in many discussions about community, trust and social networks. The multidimensionality and multivariate nature of social capital provides a foundation for explaining, although sometimes vaguely so, various social issues in communities and social networks. In most of the discussions in scientific work, social capital is continuously treated as either an output or an input. Researchers write about communities performing better due to higher levels of social capital, others attribute superior performance of social amenities such as national economy to the prevalence of higher social capital. Some writers mentioned the construct as a circumventing term to mean one or more of its core variables (trust, shared understanding, social protocols etc.) and their application to specific areas of interests, while others take a holistic view to describe all of its variables and their utility to addressing social problems anchored in communities. This Chapter discusses the application of social capital in a variety of contexts to solve community problems. This is by no means a complete comprehensive coverage of cases in which social capital is currently utilized, rather the cases presented here are considered sufficient to illustrate the growing relevance of social capital to many application areas. The goal of the Chapter is to expose the reader to key application areas and to think about the practical and theoretical relevance of social capital to research and practice in other emergent cases.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".