The Satisfaction of Tourists and Policy Guidelines for Tourism Development in Khon Kaen, Thailand
Bibliographic record
Abstract
The objectives of this research were to study the policy guidelines for tourism development in the city of Khon Kaen and the satisfaction of tourists who travel there. We have utilized both quantitative and qualitative methods for our study. Quantitatively, we provided 400 questionnaires to travelers and analyzed the subsequent data through mean and standard deviations. Qualitatively, we conducted focus groups and analyzed the data via descriptive analysis. The results of this study indicate that Khon Kaen lacks public transportation and thereby experiences numerous traffic jams. The results also show that the Khon Kaen museum is old and largely undeveloped and tourism-related activities are effectively nonexistent in the city. Local government is the main factor in the development of tourism in Khon Kaen, and it can work to renovate the museum with cooperation from the central government, set up Khon Kaen as a center for MICE (meetings, incentives, conventions, and exhibitions) business, and renovate the landscape, the city park, and the environment of the city. In addition, casinos and legal gaming need to be developed. However, it is very difficult to discuss this possibility because many people debate the need for such measures, and more time is required to resolve this issue.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".