Role of risk perception on trust, event impact experiences, and event support in the Tokyo 2020 Olympics during the COVID-19 outbreak
Bibliographic record
Abstract
The study of the risks associated with large-scale events has become increasingly prominent in recent decades. Recently, COVID-19 has emerged as an unexpected and unprecedented risk factor for hosting the Tokyo 2020 Olympics. Risk perception is widely acknowledged as a significant factor in social exchange situations where individuals rely on one another. Trust plays a crucial role in simplifying complex scenarios and facilitating the acceptance of potential risks. However, few studies have investigated the association between trust/risk perception and event experiences/event support. This study examined the effect of Tokyo residents’ trust in event organizers and risk perception in the context of COVID-19 on their event impact experiences and event support during the Tokyo 2020 Olympics through panel data analysis (n = 938). Trust and risk were measured pre-event (T1; one month before), and event impact experiences and event support were estimated post-event (T2; two weeks after). The results indicated that trust had a positive relationship with positive event impact experiences/event support, and a negative relationship with negative event impact experiences and risk perception. Risk perception was positively associated with negative event impact experiences, and mediated the relationship between trust and negative event impact experiences. Furthermore, positive and negative event impact experiences mediated the association between trust and event support. Thus, we advanced social exchange theory by demonstrating that trust is a valuable predictor of increasing/decreasing the benefits/costs of events to residents, while confirming that risk perception increases the costs of the exchange process between the event organizer and residents in mega-sporting events.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".