Bibliographic record
Abstract
Drawing on primary research within voluntary sports clubs in the UK and secondary analysis of the wider international literature on social capital, this text focuses on the micro-processes of social capital development and how they play out in specific social settings. In so doing, it adds to existing research by developing a rich, contextualised, process-based view of social capital in action. Critically reviewing theoretical and empirical literature on social capital, the book highlights the key current debates. The empirical core of the book draws on ethnographic observation over 18 months at voluntary sports clubs in the UK, including in-depth interviews with sports club members and organisers. The text explicitly seeks to set this empirical work in its wider context, by considering the findings in relation to other international studies of social capital in both sports clubs and other types of organisation. The book draws on international research from a whole range of countries: UK, USA, Australia, Canada, Norway, Denmark, Netherlands, Japan, Vanuatu, Czech Republic, Germany, and many others. The book establishes a transferable, process-based understanding of how social capital develops – both within sports clubs and beyond. This is an illuminating reading for policymakers, practitioners, and researchers with an interest in the sociology of sport, sport development, sport management, sport policy, social theory, social policy, or social networks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".