Predicting determinant factors and development strategy for tourist villages
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
Tourist village program is one development priority program for rural development. Despite numerous opportunities to develop tourist villages such as the availability of natural resources and high demand for tourist villages recently, some challenges are still faced to develop tourist villages, especially in a developing country such as Indonesia. Governance problems, infrastructure, and effective partnership are among other factors that remain challenging in developing tourist villages. This study attempts to identify factors that determine the state of tourist villages in Indonesia and determine the appropriate strategies for better tourist village development. Using the case of tourist villages in Kedung Ombo, Central Java, a water based attractive tourist village, this study uses both machine learning and multicriteria approaches by means of Promethee in order to address the objective of the study. This study shows that government support, application of information technology, infrastructure, local participation, partnership, and attractive variations, are among the determinant factors that affect tourist village development. The study also reveals that the appropriate strategies for tourist village development include, improving infrastructure, institutional strengthening, and capacity building. This study could be used to assist local national as well as sub-national governments to effectively manage tourist villages in Indonesia.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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