Implementing Sustainable Beach Tourism Management Framework for the Royal Coast Cluster, Thailand
Bibliographic record
Abstract
Beach tourism is one of the tourism models that most partners realize to manage to its sustainability. Integration of thoughts for various sectors was also needed for a walk to success. This research, thus, sought for ways to investigate for proper beach tourism management model with certain component. Testing will be implemented in area along the gulf of Thailand which is known by the name “The Royal Coast”. The mixed-methods design was employed for the study: focus group (n=88), policy meeting (n=29) and questionnaire (n=800). Both host whose stakeholders in public and privates business in the beach area and communities and guests or tourists were asked and discussed. The results from the confirmatory factor analysis (CFA) indicated that six components were the significant factors for sustainable beach management, yielding Chi-square =10.870 Chi-square/df = 1.812, df = 6, p = 0.092, GFI = 0.996, CFI = 0.993, RMR = 0.008, RMSEA = 0.032. The sustainable beach tourism included six components of management on marketing and promotion management, tourist attraction management, participation management, environmental, cultural and education management, process, plan and policy management and personnel management respectively. All six components was assigned and implemented for testing in sustaining beach tourism management on the Royal Coast.
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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 | 0.002 | 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 | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".