Philosophical Foundation, Application, and Controversies of Judicial Pardon in Islamic Criminal Law, Indonesian Penal Code, and the Criminal Justice System of Kuwait
Bibliographic record
Abstract
This article explores the philosophical foundation and scope of application of al-‘afwu ‘anil ‘uqubah in Islamic criminal law, judicial pardon in the Indonesian Penal Code, and pardon and reconciliation under the criminal justice systems of Kuwait. Adopting philosophical, statutory, conceptual and comparative approaches, it employs the judicial-normative research method to analyse legal principles, legal concepts, and legislation relevant to the subject matter and to the topic. The findings indicate that the philosophical rationale of al-‘afwu ‘anil ‘uqubah in Islamic criminal law is grounded in restorative and theological aims, limiting its application to specific offences such as qishash, certain hudud, and ta’zir. In contrast, the Indonesian Penal Code uses rechterlijke pardon to soften the rigidity of legalistic punishment, granting judges discretionary authority to withhold penalties in trivial cases, taking into account the offender’s circumstances and contextual factors. Meanwhile, the Kuwaiti criminal justice system, though influenced by Sharia principles, employs pardon and reconciliation primarily to control crime, granting extensive powers to the Amir, victims, and investigative bodies to commute or withdraw penalties in exchange for cooperation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.050 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".