Evaluating Policy Environment for Community-based Rural Tourism: Multi-Actor Perspectives in Tourism Value Chain
Bibliographic record
Abstract
Tourism policies are essential in the development of community-based rural tourism (CBRT). In practice, their implementation requires a favorable policy environment. However, the CBRT literature has not investigated the policy environment and the holistic interactions between government-community relations, inter-agency coordination, and other complex challenges relevant to CBRT policies. Involving multiple CBRT actors, this study aimed to evaluate the policy environment in the development, implementation, and evaluation of CBRT-related policies. Using Tourism Value Chain (TVC) as the conceptual framework and considering the aspects of Tourism Value Webs (TVW), this research employed qualitative interviews with government officers. In addition, this study included archival research on policy documents and questionnaire surveys among community members in multiple case studies as an added triangulation. The case studies involved 49 tourism villages in Sleman Regency, Yogyakarta, Indonesia. The regency has embraced the community-based tourism (CBT) concept for rural development by creating tourism villages. Multiple institutions in the regency work with academia and the business sector in support of the communities in developing tourism villages, forming a multiple helix structure. Despite some limitations in the policy documents, the main stakeholders in Sleman Regency can organically coordinate and cooperate to take care of all TVC phases, implying their commitment and consciousness to achieve self-reliance in CBRT development. These findings imply that the policy environment for CBRT policies is dynamic. It thus requires all stakeholders to conduct a more proactive and adaptive approach to policy evaluation, enabling enhanced support for the long-term success and sustainability of CBRT initiatives.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".