Sport Mega-Event Volunteers' Motivations and Postevent Intention to Volunteer: The Sydney World Masters Games, 2009
Bibliographic record
Abstract
Investment in mega-sport events is frequently justified on the basis that there are infrastructure and social legacies that remain after the event. This research explores the claims of a social legacy through a pre- and post-Games survey of volunteers at the Sydney world Masters Games 2009 (SwMG). Through online surveys the research explores pre-and post-volunteer motivations, postevent volunteering intentions before the Games and actual volunteer behavior after the Games. The pre-Games survey supports previous research that a desire to be involved in the event motivates people to volunteer. however, the postevent expression of motivations shifted to a more altruistic focus. The postevent volunteering intentions as indicated in the preevent survey would support the claim of a social legacy; however, this was not supported by the postevent measures of volunteering levels. The use of a pre- and postevent survey has highlighted that the timing of measures of motivations can influence responses and one may not depend on preevent intentions as an indicator of postevent behaviors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".