The Levels of Community Readiness and Community Characteristics in the Development of Tourism Village (Bangelan Village, Malang Regency, Indonesia)
Bibliographic record
Abstract
Bangelan is a village located in Wonosari District, Malang Regency, Indonesia. Bangelan Village has an area of 167.2 hectares with various natural, livestock, and agricultural potentials that support the development of tourist villages. As a tourist village, Bangelan has obstacles in tourism development due to the subordinate role of village institutions and the low capability of the community as tourism actors. This study aims to identify the community's level of readiness in developing a tourist village. In addition, the relationship between the characteristics of the community and the level of community readiness was identified. Data collection was carried out on the community and key respondents through questionnaires, interviews, and observations. The community readiness model was used to assess the level of readiness and cross-tabulation analysis and chi-square test to determine the relationship between community characteristics and the level of community readiness. The results showed that the readiness category of the community was ready with the sixth level of readiness, namely initiation. These results also show that most of the community knows and understands the basic things about tourism village development and the critical role of leaders in planning and developing businesses. The level of community readiness is influenced by characteristics including involvement in the development of tourist villages, type of work, and gender.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".