Learning to Manage Creative Tourism in Upper Northern Thailand Through Environmental Education
Bibliographic record
Abstract
The tourism industry is one of Thailand’s significant sources of income, especially in the northern part of the country, which is rich in art and nature. While tourism contributes significantly to economic growth, it also poses numerous environmental challenges. Therefore, this research had two main objectives: (1) to assess the potential of creative tourism attractions and the level of environmental perceptions, and (2) to develop an environmental education model for creative tourism management in upper northern Thailand. The study employed a mixed-methods research methodology that gathered data from 400 participants using a detailed questionnaire, 12 focus groups, and 12 in-depth interviews for data collection and analysis. The results indicated high levels of creative tourism attractions (4.07 ± 0.88) and environmental perceptions among locals, creative tourism hosts, and stakeholders (3.79 ± 0.92). The collected data built an environmental education model with seven essential components for managing creative tourism. The established components included: (1) enhancing environmental consciousness, (2) intriguing creative tourism destinations, (3) appealing creative tourism activities, (4) skilled personnel in creative tourism, (5) active participation by the locals, (6) an integrity-based creative tourism network, and (7) experiential marketing. The model was further implemented in a workshop for locals, tourism hosts, and stakeholders, yielding a high-efficiency evaluation (4.21 ± 0.92). We hope that the positive correlations between environmental perception and creative tourism attractions will aid the protection and restoration of environmental degradation, especially in areas with creative tourism.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".