It's All About the Games! 2010 Vancouver Olympic and Paralympic Winter Games Volunteers
Bibliographic record
Abstract
Despite volunteers being essential for the success of many mega sport events, there is little known about what motivates them to volunteer at such events. This study aims to address this gap. This article commences by developing getz's event portfolio into a new expanded sport event typology. It continues by presenting the results to three key questions: (1) who is volunteering? (2) what are their motivations for volunteering, and (3) what variables are most likely to be related to their intention to volunteer after the event. The study used an adaptation of the Special Event Volunteer Motivation Scale on volunteers at the 2010 Vancouver Olympic and Paralympic winter games. A principal components analysis of the 36 motivation items identified six factors that accounted for 58.3% of the variance, with the main factor entitled "All about the games." A regression analysis conducted to identify those variables most likely to indicate an intention to volunteer more after the games demonstrated that those who could see an advantage in more volunteering pregames were most likely to intend to increase their level of volunteering postgames. People with previous volunteering experience in events, sport, or community groups were less likely to indicate they would volunteer more after the event. The results and recommendations have implications for mega-multisport event organizing committees not just in respect of event delivery but in terms of a post-event volunteer legacy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".