Sustainable Coastal Tourism: A Comprehensive Development Strategies (Tanjung Bira and Lemo-lemo Tourism Area as a Case Study)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Tourism development has become an effective way to improve the economy and welfare of local communities in many areas, especially areas with high tourism potential.This research aims to formulate a tourism management strategy using SWOT Analysis and provide recommendations based on sustainable coastal tourism in the Tanjung Bira and Lemo-lemo tourism areas.The data collection method in this research uses primary and secondary data.Primary data was obtained by participatory observation and interviews.Secondary data was obtained by document review, which collected information related to policies, history, journals, and literature related to tourism.Data analysis was conducted using SWOT analysis, descriptive-qualitative, and comparative study to determine strengths, weaknesses, opportunities, and threats in formulating tourism strategies.Moreover, descriptive-qualitative analysis is used to formulate policies related to strategy based on sustainable coastal tourism.Based on an analysis of 35 internal and external factors, the coastal tourism development strategy can be carried out with the S-O Strategy (Integration between tourist locations, increasing the role of government and fulfilling vegetation), W-O Strategy (Improving facilities and infrastructure, Community-government cooperation, tourism promotion).S-T strategy (Improvement of regulations, accessibility, and community empowerment), W-T Strategy (Arrangement and direction of planning tourist areas).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it