Cross-Cultural Comparsion of International Tourist Destination Images
Bibliographic record
Abstract
The researchers undertook an empirical examination of the tourist images of Taiwan prior to and after visiting a Taiwanese tourist night market. A survey was distributed at Shilin night Market and generated 230 responses from Japanese, 171 from US/Canadian, and 95 from Mandarin-speaking Chinese tourists. The data analysis indicated significant differences between the trip characteristics of respondents from the three groups. The images held by visitors from Japan and Hong Kong/Macau/China were found to be more positive after than before they visited the night market. However, the tourism images held by visitors from the US and Canada were the same after as before visiting. The results indicated that the changes between induced (before the trip) and complex tourism images (after the trip) varied on the basis of nationality, age, occupation, education, and income. The researchers suggest that the Taiwan Tourism Bureau and organizations associated with the tourist night market should implement marketing strategies targeted at international tourists, including promotion and product development, on the basis of their nationality and/or 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".