Cognition and Assessment of Tourism Disaster Risk Based on a Tourism Spot of Green Island
Bibliographic record
Abstract
In this study, we examined the risk of offshore travel from the dimensions of tourists and purveyors in the tourism industry. A questionnaire survey was administered for data collection. A factor analysis was performed to determine respondents’ perceptions, evaluations and responses, and demands and intentions concerning travel risk, as well as the degree of hazard impact. The analysis results were then used to investigate the similarities and difference of travelers’ and tourism purveyors’ travel demands. Survey analysis results indicated partial significant differences between travel behaviors and travel risk awareness and travel risk evaluations and responses. In addition, travel risk awareness was partially correlated to travel risk evaluations and responses, travel risk demand and intentions, and degree of hazard impact. Respondents with higher travel risk awareness were more careful in evaluating hazard risk, consequently influencing their tourism and travel behaviors. Applying the analysis results, we addressed traveler-related, operator-related, and environment-related travel risk factors proposed a response strategy for minimizing travel risk, helping parties in the tourism industry cope with hazards and minimizing the risk and losses associated with hazards.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".