Implementation of Food Estate for Improving The Community Welfare from The Perspective of Agrarian Reform in Indonesia
Bibliographic record
Abstract
This study aims to analyze the implementation of food estates in improving community welfare from an agrarian reform perspective. Additionally, it seeks to identify the obstacles to implementing food estates in enhancing community welfare. A food estate aims to achieve food security for improving community welfare. In Bansari Subdistrict, Temanggung Regency, Indonesia, the Food Estate program utilizes existing land for horticulture without opening new land. However, in a broader context, several issues regarding this program have led to it being considered less implementable and potentially contradictory to the concept of welfare from an agrarian reform perspective. Answering the problem, this field study employed an empirical legal approach and theories of natural resource management, legal benefit theory, and welfare theory. The results show that implementing food estates in Bansari District, Temanggung, has improved community welfare from an agrarian reform perspective. One of the reasons for the program's success is that it is targeted and does not open new land, taking into account aspects of nature conservation and sustainability of benefits for farmer groups. The challenges faced by the food estate in Bansari District, Temanggung Regency, include internal conflicts among farmer groups due to poor communication, imbalances in the implementation of the food estate program, and a lack of information regarding data on farmer groups receiving subsidies from the food estate program.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".