Community-Based Tourism: A Strategy for Sustainable Tourism Development of Patong Beach, Phuket Island, Thailand
Bibliographic record
Abstract
This study proposes community-based tourism as a strategy for sustainable tourism development of Patong Beach. Direct observation, questionnaire and interview are research instruments. A result of analyzing 120 questionnaires of local people which displayed a negative impact including economic impact which was very high )= 4.53(, social impact )= 4.28( and environmental impact) = 4.42( which were high so the total mean score was high )= 4.41(. The Community-Based Tourism was adapted for solution all negative impacts which were mentioned earlier. The sreategies are namely 1. Political development strategy: (1.1) Enabling the participation of local people, (1.2) Giving the power of the community over the outside and (1.3) Ensuring rights in natural resource management. 2. Environmental development strategy: (2.1) Studying the carrying capacity of the area, (2.2) Managing waste disposal and (2.3) Raising awareness of the need for conservation.3. Social development strategy: (3.1) Raising the quality of life, (3.2) Promoting community pride, (3.3) Dividing roles fairly between women/men, elder/youth and (3.4) Building community management organizations. 4. Cultural development strategy: (4.1) Encouraging respect for different cultures, (4.2) Fostering cultural exchange and (4.3) Embedding development in local culture and 5. Economic development strategy: (5.1) Raising funds for community development, (5.2) Creating jobs in tourism and (5.3) Raising the income of local people.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.004 | 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".