Relationship between volunteer motivation and the achievement of the volunteer concept at the Tokyo 2020 Olympic Games during the COVID-19 pandemic
Bibliographic record
Abstract
Despite volunteers being essential to the success of the Olympic Games, research on Olympic volunteers’ motivations remains limited. The aim of this study was to investigate this phenomenon with volunteers at the Tokyo 2020 Olympic Games during the COVID-19 pandemic. The results of a questionnaire survey of 546 volunteers identified three dimensional motivations (i.e., relationship with others, love of the Olympics and sports, and esteem needs) that drove them to become Olympic volunteers. These findings clearly differ from Western society, as illuminated by the previous studies which revealed that resume material and networking were key factors in volunteerism at Olympic Games held in Sydney in 2000 Athens in 2004, Vancouver in 2010, and London in 2012. It became clear that the greatest influence on the achievement of the Tokyo 2020 volunteer concept of ‘I will shine’ were love of Olympics and sports, and esteem needs. The volunteers’ endorsement of holding the Olympic Games during the COVID-19 pandemic was supported by relationship with others and affection of the Olympics and sports. The findings and recommendations have ramifications for the future organizing committees of the Olympic and Paralympic Games in terms of volunteer participants by considering cultural background.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".