Identifying the determinants of tourism receipts of Thailand and relevant determinant-determinant interactions
Bibliographic record
Abstract
The study examined the determinants that affect tourism receipts in Thailand. To this end, quarterly data from eight main provinces of Thailand from the period 2015-2019 were used and constituted a repeated measures design. Accordingly, a generalized linear mixed model was applied for developing two different random intercept models by treating 1) province, and 2) a combination of province and calendar quarter as cluster-specific effects. It was found that determinants that increased tourism receipts were the number of visitors, the average cost per day, the length of stay of visitors, the presence of low-cost airlines, and a relatively low offence rate. Moreover, an increase in the number of visitors in the fourth quarter produced a higher amount of additional receipts as compared to a similar increase in the first quarter. Specifically, for Thailand attracting high-spending tourists and extending tourist visas for more than 30 days is recommended. Beyond Thailand, uncovering interaction effects as described above may help tourism agencies to focus their limited resources on the determinants that matter.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".