Tourism and Ethnodevelopment: Female Contribution in Rural Community-Based Agritourism
Bibliographic record
Abstract
The ethnodevelopment emphasizes cultural dimension as identity form, the encouragement of local and marginalized groups in decision-making, and the crucial role of the indigenous knowledge and skill to sustain local livelihood. In contrast, females in rural areas have the same potential as men. It is then necessary to empower females with many ethnic backgrounds as farmers. The Women Farmers Group is a forum consisting of women engaged in agricultural activities within various ethnic groups. In addition, females expect to play a role in economic empowerment to achieve financial independence and build their territory. This research uses a qualitative perspective to illustrate Community Based Agritourism, which dominated ethnic lives in two rural areas of the Pesawaran Regency, and the female characteristics among farmers groups to promote the territory by linking social capital from many stakeholders. Agritourism initiates the diversification of agriculture products and supports the revitalization of rural areas. It is also an important instrument to improve the social status of the female. Both local and national levels in agritourism development need to promote female contributions. The assistance of Women in economic empowerment requires solid institutional support. Furthermore, it emphasizes state institutions as the key players in agritourism development.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".