Analysis of Pentahelix Tourism Village for Ecotourism Development in Batu City, East Java
Bibliographic record
Abstract
East Java Province, particularly Batu City, is a significant tourism hub offering opportunities for investment in artificial and ecological attractions.Batu City is adopting sustainable tourism through ecotourism, emphasizing environmental preservation, community empowerment, and socio-economic benefits.However, unplanned development of tourist villages can lead to negative impacts such as environmental damage and cultural erosion.Effective ecotourism management requires active community involvement and coordinated stakeholder efforts.This study examines the role of Pentahelix-comprising government, private sector, academia, media, and community-in developing sustainable ecotourism-based tourist villages.The research identifies key Pentahelix elements influencing this development.Data was collected through surveys, interviews, and observations involving village leaders, tourism community organizations, academics, media, and investors.The study utilized Interpretative Structural Modeling (ISM) for analysis, supported by Exsimpro software.ISM provided a systematic framework to prioritize and understand interactions among variables, offering actionable insights for stakeholders.Findings reveal that successful ecotourism development depends on five key variables: regulations and policies, research and development, private investment, community participation, and media reach.Clear rules, thorough research, private sector investment, active community involvement, and effective media strategies are crucial for optimizing sustainable ecotourism benefits and ensuring the growth of tourist villages.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".