The relationships among the five key foreign tourist markets in the Greater Mekong Sub-region
Bibliographic record
Abstract
The objective of this research was to examine the relationships of the five key foreign tourist markets in the Greater Mekong Sub-region (GMS), Cambodia, Laos, Myanmar, Thailand, and Vietnam. Quarterly data of tourist numbers from China, Japan, Korea, Malaysia, and the USA who traveled to the countries in GMS during the period 2001-2017 were used. Vector Autoregressive (VARs) was used to test Granger Causality to explain the relationships among tourist markets in the region. The findings revealed that tourist markets in Cambodia had forward relations with Laos and Vietnam. Those in Laos had forward relationship with Myanmar and Thailand. Tourist markets in Myanmar had forward relationship with Vietnam, Laos, and Thailand. Tourist markets in Thailand had forward relationship with Vietnam, Laos, and Myanmar whereas those of Vietnam had forward relationship with Laos, Myanmar, Thailand, and Cambodia. The overview shows that the key tourist markets in each country supported each other.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".