Transformation a Poor Village into a Prosperous Tourist Destination in Indonesia
Bibliographic record
Abstract
A new leader has inspired young people to transform an agrarian village into a popular tourist destination.The goal of this research is to explain the process of transforming an agricultural village into a tourism village, including the role of the Village Head (VH) and other actors involved.This research was conducted in Pujon Kidul (PK) village, Pujon subdistrict, Malang Regency, East Java Province, Indonesia.Data was collected through in-depth interviews with innovators, small traders, and members of society at tourist sites.Apart from conducting indepth interviews, secondary data was also utilized to examine how the innovative and entrepreneurial spirit of innovators can transform the traditional lifestyle of PK Village.This phenomenon can be explained by the Knowledge-intensive Innovative Entrepreneurship (KIE) framework, which highlights the significance of knowledge and innovation in entrepreneurial development.The study's findings enhance the KIE framework by reinforcing the significance of entrepreneurial leaders and real political support.Moreover, the government's policies that actively promote the growth of rural areas and tourism are external factors that encourage Village Heads to bring about social transformation.These policies have created an environment that nurtures the entrepreneurial spirit of all members of society.Contrary to popular belief, this article argues that village development from the grassroots level is an achievable goal.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".