Predicting Intention to Volunteer for Mega-Sport Events in China: The Case of Universiade Event Volunteers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Attracting and retaining a loyal base of volunteers is critical to the success of mega-sport events (MSEs). The purpose of this study was to examine the antecedents of MSE volunteering in a Chinese context. Drawing upon self-determination theory, the study establishes a valid structural equation model of antecedents of Chinese volunteers' satisfaction and their intention to volunteer in future MSEs. The XXVI Summer Universiade provides a case-specific context. After a pilot study to validate questionnaire items, location-based convenience sampling was employed to collect data from Universiade volunteers. A total of 1,015 questionnaires were completed and analyzed. Results from the covariance-based structural equation modeling analysis showed that all of the three exogenous factors—external attractiveness, altruism, and intrinsic motivation—emerged as significant predictors of volunteer satisfaction. In turn, volunteers' perceived level of satisfaction predicted future MSE volunteer intention. Our findings reveal unique differences between Chinese sport event volunteers and their Western counterparts. Implications for event planning and volunteer program design are discussed.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it