Dilematisasi Pemberian Remisi Bagi Narapidana? Formulasi Berdasarkan Studi Perbandingan Inggris, Irlandia, dan Kanada
Bibliographic record
Abstract
As a country with an overcapacity prison composition of 265,897 people, Indonesia ranks seventh with the most prisoners in the world. In response to this, the government has made several efforts to reduce the density of prisoners, one of which is by providing remissions. However, granting remissions is actually seen as less effective and actually creates differences in the development process in Correctional Institutions (Lapas). Another problem is how to overcome the dilemma of granting remission to prisoners from the perspective of the national legal system. The research method used in this research is a juridical-normative research method with descriptive analytical research specifications which analytically describe the applicable laws and regulations both at home and abroad and legal theories linked to research problems. Analysis of legal materials uses qualitative juridical analysis. The results of this research indicate that the background to the policy of granting remissions to prisoners needs to be tightened so that it can fulfill a sense of justice for society. Apart from that, regarding the policy of granting remissions to prisoners, it is necessary to consider the legal framework of similar policies implemented in England, Ireland or Canada because the tightening of remissions in these countries has resulted in not all prisoners getting remissions or parole.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".