Bibliographic record
Abstract
Regardless of any approach taken for examining social capital, researchers continuously converge on some key issues such as trust and yet diverge on several others about concrete and consistent indicators for measuring social capital. Many researchers believe that presence or absences of social capital can be solely linked to trusting relationships people build with each other as well as social institutions of civil engagement. It is not clearly known however, whether trust itself is a precondition for generating social capital or whether there are other intermediary variables that can influence the role of trust in creating social capital. In addition, similar to social capital, the definition of trust is problematic and it remains a nebulous concept and equally, with many dimensions. Interests in the analysis of trust are wide spread among many disciplines, notably policy analysis, economic development, reliability and security of distributed computational systems and many others. The variety of approaches currently employed to investigate trust and different interpretations of its role in fostering social capital has resulted into a diverse array of knowledge about the concept and its relationship to social capital. This Chapter provides a broader overview of work on trust. It discusses how researchers have used trust as a proxy for measuring social capital.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".