The Construction of Tour Guide Application to Enhancement and Multilingual Tourism Development in Mahasarakham Province, THAILAND
Bibliographic record
Abstract
This study investigates the development and efficacy of a multilingual tourism application designed to enhance tourism development in Mahasarakham Province, Thailand, focusing particularly on its utility for foreign students due to the region’s educational allure and cultural heritage. The application features a comprehensive suite of services, including weather information, personalized travel recommendations, navigational assistance, and detailed local guides covering dining, healthcare, lodging, cultural practices, and historical insights, thereby distinguishing it from conventional tourism apps. The results of the study reveal significant outcomes. Expert evaluations yielded a Content Validity ratio (Content validity) of 0.86, indicating high relevance and accuracy of the app’s content. The application’s Reliability stood at 0.91, reflecting its consistency and dependability. Additionally, the Objectivity score was 0.80, suggesting the app’s impartiality and fairness in presenting information. These results affirm the application’s effectiveness in providing a reliable, accurate, and user-friendly resource for enhancing tourists’ experiences while promoting cultural understanding and economic development within Mahasarakham Province.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".