Diseminasi Model Pemberdayaan Masyarakat Desa melalui Pengelolaan Agrowisata
Bibliographic record
Abstract
Diversion of agricultural land to agrotourism areas urge the villagers into a trap landless and unemployment, which in turn have an impact on sustained poverty. Therefore, this study aims to examine sosial relations in the dissemination model of empowering rural communities through local resource management of agrotourism. The research location is set intentionally in the four areas into the development of agrotourism in the central region Banyumas and Purbalingga. The research method used survey with qualitative and quantitative approach. The research’s results showed there was a variety of external and internal issues that hinder the dissemination of sosial relationships communicative empowerment model. However, the villagers have attempted to solve the obstacles according to the capability of local resources. Sosial relations have a significant meaning in the process of dissemination on rural community empowerment model through agrotourism management.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Survey of rural community empowerment through agrotourism management; a development studies question.
The work studies rural-community empowerment through agrotourism management.
Rural community empowerment via agrotourism management; development studies, not metaresearch.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".