Design of Islamic Agricultural Insurance Model: Evidence from Indonesia
Bibliographic record
Abstract
The agricultural sector is a role model that can be the basis for policymakers to integrate Islamic insurance patterns in agriculture. This research aims to design a model of the Islamic agricultural insurance system based on Islamic law in the context of Indonesia. This study is expected to contribute for Indonesian government to accommodate the implementation of the Islamic agricultural insurance system and pattern in Indonesia. This study used an expert system approach through data collection and information as a reference in the formulation of model designs, including interviews, questionnaires, direct observation, and data synthesis in the field. This research stage used a systems development conceptual model described in the research process, including planning, analysis, design, verification, validation, and building models. The research hypothesis is confirmed that Islamic agricultural insurance can be implemented based on risk and investment with tabarru’ funds (mutual financial aid funds) and investment funds. Islamic agricultural insurance is a way out for farmers, especially Muslim farmers, in ensuring agriculture management by managing the level of risk due to crop failure. Islamic agricultural insurance systems can guarantee risks that arise in agricultural businesses to provide inner peace to farmers in getting the good and right protection for businesses that run following Islamic principles. This research has resulted in innovative features and development models of Islamic agricultural insurance for the Protection and Empowerment of Farmers in Indonesia.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".