Generating Social Capital: Civil Society and Institutions in Comparative Perspective
Bibliographic record
Abstract
Generating Social Capital: Civil Society and Institutions in Comparative Perspective, Marc Hooghe and Dietlind Stolle, eds., New York and Houndmills, Basingstoke: Palgrave Macmillan, 2003, pp. 256 The concept of “social capital” has become a popular buzzword. Like other authors, the contributors to this volume draw on Robert Putnam's well-known definition of social capital as “generalized trust, norms of reciprocity and networks” among individuals (2). Social capital is credited with providing a wide range of social benefits, including tolerance of diversity, economic growth, lower crime rates, better health and more responsive government. The grandiose claims made on behalf of social capital and the large amounts of money being poured into developing social capital in diverse social settings, as well as the fuzziness of the original concept, mean that careful analysis of the idea of social capital is badly needed.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".