The power of play: uncovering the hidden impact of sport events on communities
Bibliographic record
Abstract
Sport tourism, despite being recognized as an effective activity for facilitating recovery from the COVID-19 pandemic, primarily emphasizes benefits related to economic aspects based on current research findings. However, the impacts of sports events extend beyond financial benefits, to various community benefits such as building community cohesion, fostering civic pride, and promoting progressive values. The perceptions of residents within hosting communities also significantly impact the success of these events, yet there are limited empirical studies. To address this research gap, this study aims to investigate the socio-psychological impacts of sports event hosting to address this gap. By introducing a theoretical framework and validating it empirically using 589 responses from a survey conducted among residents in the City of London, Canada, this study sheds light on the indirect relationship between psychic income and quality of life through an SEM analysis. Additionally, psychic income was found to have a significant influence on active support, indicating that residents who possess a strong emotional connection to the sports event are more likely to offer robust support. Ultimately, this study supports managers in the sport-hosting communities to obtain better resources and provide empirical measurements for the social impacts of the sport hosting, guiding hosts to engage fully with their communities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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".