Leveraging long-term sport participation from major events: the case of track cycling after the 2015 Pan Am/Parapan Am Games
Bibliographic record
Abstract
Rationale/Purpose: This study examined long-term sport participation outcomes of an event leveraging strategy.Design/methodology/approach: After watching elite track cycling events at the 2015 Pan Am/Parapan Am Games, spectators (n = 176) received a voucher for a complimentary “Try the Track” trial program at the host facility. Data assessed voucher recipients’ participation at the host facility one-year following the event. Analyses addressed how participation in a trial, structured, or both programs led to (or did not lead to) facility membership.Findings: Of the 176 voucher recipients, 35 participated in a program and one became a member. Results indicated participation in a trial or structured program did not lead to membership, while participation in both programs did.Practical Implications: Findings indicate the need for practitioners to communicate and engage with new participants throughout the entire leveraging process. Results demonstrate the importance of program design features and how practitioners can effectively employ event leveraging strategies to create long-term sport participation.Research Contributions: This study measures the efficacy of an event leveraging strategy to create long-term sport participation outcomes. Findings provide evidence for how event leveraging strategies can, and cannot, create desired long-term sport participation outcomes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".