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Record W4403627743 · doi:10.1080/14775085.2024.2414961

The power of play: uncovering the hidden impact of sport events on communities

2024· article· en· W4403627743 on OpenAlexaffabout
C.E. Finn, Lena Jingen Liang, H. S. Chris Choi

Bibliographic record

VenueJournal of Sport & Tourism · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsUniversity of GuelphUniversity of Prince Edward Island
Fundersnot available
KeywordsPower (physics)TourismBusinessMarketingAdvertisingPolitical science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.280
Threshold uncertainty score0.505

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.328
Teacher spread0.304 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes2
Has abstractyes

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