IMPLEMENTASI KEBIJAKAN PROGRAM BANTUAN HUKUM BAGI MASYARAKAT TIDAK MAMPU DI KABUPATEN KUNINGAN
Bibliographic record
Abstract
Law enforcement on the one hand and justice in the community on the other hand is necessary to harmonize, especially in the right to get legal aid for the community by not discriminating on race, religion and class.The formulation of the problem How the regulation of legal aid for the poor people in districts kuningan and How the implementation of legal aid for the community can not afford in kuningan regency.The purpose of this study is to find out how the regulation of legal aid for the poor people in kuningan district and find out how the implementation of legal aid for the community can not afford in kuningan regency.The research method used by the researcher is using empirical juridical approach.The result of the research of legal aid arrangement for the poor people in kuningan district is through legal aid activities based on the activities in the legal part of the local government of kuningan regency based on Law number 16 of 2011 on Legal Aid and the implementation of legal aid for poor people in kuningan regency ie government kuningan district district to make a letter of agreement with legal aid organizations in the region III Cirebon in providing assistance criminal cases involving residents kuningan district.The conclusion of this study is that local governments have provided legal aid for the poor and need to be improved by making local regulations on legal aid, increasing legal aid funds and optimizing the involvement of legal aid organizations in the kuningan region.
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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 | 0.003 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.075 | 0.008 |
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".