Bibliographic record
Abstract
This review paper focuses on volunteers in community sports associations (CSAs). Such associations are a major context of sports volunteering across Europe, Canada and Australia—the countries in which a multitude of sports clubs are represented by governing bodies of sport. Their importance is not only in the large numbers of volunteers involved but also in the benefits of such associations to society. The clearest of these is the provision of opportunities to take part in sport, at a cost subsidized by the efforts of volunteers and thus contributing to physical health. However, the benefits extend more broadly to the quality of life and the rewards the volunteers themselves receive from association. Many community sports associations have a significant number of members who, while they do not actually play sport themselves, provide opportunities for others and also enjoy the social rewards of membership. The aim of this broad-ranging review is to introduce the reader to community sports associations as an example of small, volunteer-led associations, and to make links between academic theory in this area and the more general study of volunteering. The breadth of the review allows readers to follow-up supporting references on individual topics. The author's extensive experience, primarily of England and Europe, has inevitably led to more examples being drawn from these areas; however, broader international work is also incorporated. It is hoped this review will stimulate readers' thinking about volunteering in their own country.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".