Cognitive factors that lead to inspiration and post-event intention to swim among spectators
Bibliographic record
Abstract
Rationale/purpose The purpose of this study was to explore the role that event cognitions (aesthetics, evaluation, fantasy, flow, personalities) and inspiration play in generating a demonstration effect for spectators at one elite-level swimming competition, while controlling for spectators having other physical activity preferences, age, and current swimming participation status.Design/methodology/approach Data were collected from spectators (N = 258) via a questionnaire at one elite swimming competition in Canada. Structural equation modeling was used to analyze the data.Findings Results indicated that inspiration had a direct association with intentions. Furthermore, inspiration mediated the positive relationships that fantasy and aesthetics had with intentions. All of the significant relationships were able to form notwithstanding the influence of the control variables.Practical Implications Suggestions to inspire spectators and increase post-event intentions are offered, including a focus on stimulating spectators to fantasize they are part of the action and highlighting the aesthetics of the sport.Research Contribution Although previous research has indicated that non-swimmers or those who prefer other physical activities are less likely to participate in the sport post-event, results suggest that those conditions might not have an effect on the formation of inspiration.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".