Strengthening Tourist Village Attractions Through Empowerment of Rural Micro, Small, and Medium Enterprises (MSMEs)
Bibliographic record
Abstract
One of the efforts to improve the rural economy can be conducted by developing tourist villages. The attractions of tourist villages that all this time have relied on natural potential are apparently not capable enough to guarantee the sustainability of tourist villages to be existing and favoured by the tourists. There should be innovative attractions so that the tourists have the will to make return visits and increase the length of stay. This research aims at developing a model for empowering the rural MSMEs in strengthening the attractions of tourist villages. This research is the qualitative one, which uses the primary data taken using observation techniques and Focus Group Discussions. The analytical method used in this research is the Delphi method and qualitative descriptive. The Delphi method was used to develop a model for empowering MSMEs by involving experts, while qualitative descriptive analysis was used to explain the mechanism for implementing the model. The implementation of the model has been tested in a tourist village in Central Java. This research has found that the right model for empowering the rural MSMEs to support the attractions of tourist villages can be conducted by establishing partnerships between the owners of rural MSME and the manager of tourist village. In Candirejo Tourist Village this model has been implemented. The production activities of MSMEs become a showcase, and are also able to increase the activities of tourists who stop at tourist villages. The results of implementing the model show that empowering the MSMEs owned by the community as an attraction to be visited by tourists is actually able to significantly increase the income of the owners of MSMEs, and to increase the tourists' interest in visits and length of stay.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| 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".