Strategi Kolaborasi dalam Pengembangan dan Keberlanjutan Pasar Tematik Wisata Jelajah Danau Ranau Lumbok Seminung Kabupaten Lampung Barat
Bibliographic record
Abstract
The West Lampung Regency Government has allocated IDR 70 billion for the development of the Lake Ranau Lumbok Seminung Thematic Tourism Market as part of its tourism-based economic development strategy. This market is designed around the concepts of tourism, culture, and shopping to attract visitors and enhance the welfare of local communities. However, the primary challenge lies in ensuring the market's sustainability and effective utilization so that it does not experience the same fate as previous tourism facilities in the area, which suffered from suboptimal usage and sustainability. This policy paper employs a qualitative descriptive approach, utilizing Focus Group Discussions (FGDs), field studies, and policy analysis. The study’s findings indicate that the development and sustainability of the market require robust regulations, cross-sector collaboration, and community empowerment. Therefore, it is recommended that a Regent Regulation be enacted to systematically manage thematic markets, integrate a tourism curriculum into local education, and prioritize the designation of market locations in community and MSME empowerment programs. The implementation of this policy is expected to support inclusive economic growth, enhance the attractiveness of regional tourism, and ensure the sustainability of thematic tourism markets as strategic assets for West Lampung Regency.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.006 |
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".