Structure Requirements for Developing the Insurance Program Adoption for the Rice Farming Business in Banyuwangi Regency
Bibliographic record
Abstract
Background: The farming businesses play an essential role in contributing to Banyuwangi Regency’s standard of living. Agriculture is also associated with food security. Rice plants are the most extensively cultivated food crop in the community. It is the background of why the Indonesian government established Asuransi Usahatani Padi (AUTP) or Rice Farming Insurance (RFI). RFI program is designed to help protect farmers from crop failure-related losses. This study intends to analyze the requirements that must be met to establish the RFI in Banyuwangi. Methods: Observation and research during 2020-2022. The focus of the investigation is farmers who use and do not use agricultural insurance in Kabat District, Banyuwangi Regency. Combining quantitative and qualitative approaches. 35 farmers and stakeholders were selected to collect data and 7 experts with knowledge and capacity to understand rice cultivation and social issues were involved in the evaluation using the analytical tool Interpretive Structural Modelling is a research method. Result: The indicated that systemically implemented extension services, the simplicity of RFI financing techniques and procedures and the strengthening of agricultural institutions and partnerships are essential for promoting RFI adoption.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".