The Protection and Empowerment of Farmers: Legal Policy Framework Beyond Farmer Insurance
Bibliographic record
Abstract
The welfare paradigm for the community is always related to the policy model that will be issued by the government and local governments. Legal policy is an instrument in an effort to provide justice, legal certainty and benefits for the community. Indonesia as an agrarian country needs protection for the development of the farming system for farmers by providing welfare guarantees for farmers. This is inseparable from the legal instruments that must be owned by the state in order to encourage concrete protection to farmers in the event of crop failure. Law of the Republic of Indonesia No. 19 of 2013 on the Protection and Empowerment of Farmers is a basic instrument in an effort to provide protection to farmers, especially on farmer insurance and the existence of the Minister of Agriculture Regulation No. 40 of 2015 on the Facilitation of Agricultural Insurance is a derivative of the protection of farmers. But the current problem is needed to strengthen the understanding of farmers to be part of the legal policy efforts of farmers in order to provide protection to farmers. This research uses a qualitative method with a normative approach and analyzes in depth the existing conditions.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".