Conditions under Which Trickle-Down Effects Occur: A Realist Synthesis Approach
Bibliographic record
Abstract
Policy makers often legitimize bids for major sport events and public funding of elite sports by trickle-down effects, suggesting that hosting events, sporting success, and athlete role models inspire the population to participate themselves in sport and physical activity. According to previous review articles, empirical evidence of trickle-down effects are mixed, with several studies citing marginal or no effect. The purpose of this study is to apply a realist synthesis approach to evaluate under which conditions trickle-down effects occur (i.e., what works for whom under which circumstances?). Using rapid evidence assessment methodology, 58 empirical articles were identified in the search process and critically analyzed through the lens of realist synthesis evaluation. The analysis identified six conditions under which trickle-down effects have occurred: Event leveraging initiatives, capacity of community sport to cater for new participants, live spectating experiences, consumption possibilities on television or other media, and communities housing event venues. The findings have implications for the sustainability of sport policy decisions and public finance, as the likelihood of trickle-down effects increases with integrated planning and sustainable spending related to the above six conditions.
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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 | 0.217 | 0.390 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.027 | 0.019 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".