Community based tourism logistics supply chain management in the top north of Thailand: A key linkage to the greater Mekong subregion
Bibliographic record
Abstract
Thailand’s tourism is one of the primary revenues generating industries because of its geographical and richness of natures & cultures. However, some remote destinations require development such as routes, tourism products, and markets. Therefore, the aims of this study are to examine logistics of community-based tourism (CBT) destinations and identify key success of CBT logistics. The inductive method was applied for the insight details and fostering by the quantitative including on-site survey, participants observation, in-depth interview, focus group, Participatory Rural Appraisal (PRA) with tourism stakeholders were conducted. Whereas the questionnaires were served for quantitative methods. The data was collected from 721 tourists onsite. The content analysis and Geographic Information System (GIS) were administered analyzing the qualitative data. On the other hand, the structural equation modelling was applied for a quantitative approach. The result revealed that CBT in the top north of Thailand was faced with logistics problems especially in aspects of accessibility and amenities in the travelling route. Thus, this paper presents a model of CBT logistics management in relation to the Greater Mekong Subregion GMS region.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".