Large-scale sport events and well-being: Exploring residents’ pre-event perspectives
Bibliographic record
Abstract
Large-Scale Sport Events (LSSE) are increasingly contested as host cities balance the benefits and costs of these events. Residents are a stakeholder group heavily impacted by LSSE given their rootedness in the host city over the event lifecycle spanning: bid submission, awarding of host rights, pre-event planning, event delivery, post-event shutdown and beyond. Yet, limited focus has been placed on understanding residents' perceptions of LSSE impacts in the pre-event period. Twenty-seven semi-structured interviews, guided by Seligman’s (2011) PERMA framework, were conducted with Australian residents in 2023 FIFA Women’s World Cup host cities. Findings demonstrated how four PERMA domains (positive emotions, relationships, meaning and accomplishment) were activated. Findings advance understanding of how LSSE can positively impact residents in a phase of the event lifecycle when event organisers and host governments are often not yet actively engaging with this key stakeholder group. Key theoretical and practical implications are discussed.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".