Sport participation from sport events: why it doesn’t happen?
Bibliographic record
Abstract
Purpose The purpose of this paper is to present and use an event leveraging framework (ELF) to examine processes and challenges when seeking to leverage a sport event to build sport participation. Design/methodology/approach The study used an action research approach for which the researchers served as consultants and facilitators for local sports in the context of the International Children’s Games. Initially three sports were selected, and two sports were guided through the full leveraging process. Prior to the event, actions were planned and refined, while researchers kept field notes. Challenges and barriers to implementation were examined through observation immediately prior to and during the event, and through a workshop with stakeholders six weeks after the event, and interviews a year later. Findings With the exception of a flyer posted on a few cars during the track and field competition, none of the planned action steps was implemented. Barriers included competition and distrust among local sport clubs, exigencies associated with organizing event competitions, the event organizers’ focus on promoting the city rather than its sports, and each club’s insufficient human and physical resources for the task. These barriers were not addressed by local clubs because they expected the event to inspire participation despite their lack of marketing leverage. The lack of action resulted in no discernible impact of the event on sport participation. Research limitations/implications Results demonstrate that there are multiple barriers to undertaking the necessary steps to capitalize on an event to build sport participation, even when a well-developed framework is used. Specific steps to overcome the barriers need to be implemented, particularly through partnerships and building capacity for leverage among local sport organizations. Originality/value This study presents the ELF, and identifies reasons why sport events fail to live up to their promise to build sport participation. Necessary steps are suggested to redress that failing.
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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 | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".