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Record W4413005057 · doi:10.18535/sshj.v9i08.1961

Bridging Restorative Justice and Fair Trial: Reconstruction of Closed Trials in the Indonesian Criminal Justice System

2025· article· en· W4413005057 on OpenAlexaboutno aff
Zoya Haspita, Erna Dewi, Ahmad Irzal Fardiansyah

Bibliographic record

VenueSocial Science and Humanities Journal · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicLegal and Policy Analysis in Indonesia
Canadian institutionsnot available
Fundersnot available
KeywordsRestorative justiceIndonesianBridging (networking)Criminal justiceCriminologyFair trialEconomic JusticePolitical sciencePsychologyLawComputer securityHuman rightsComputer sciencePhilosophy

Abstract

fetched live from OpenAlex

This study aims to formulate a selective closed trial model that allows for the implementation of restorative justice without violating the principle of openness. Restorative justice has become an alternative approach in the Indonesian criminal justice system that places victims at the center of recovery. However, the implementation of restorative justice faces challenges when confronted with the principle of openness in trials, a fundamental principle in national criminal procedure law. The method used in this study is normative juridical with statutory, conceptual, and comparative approaches. By examining provisions in the Criminal Procedure Code (KUHAP), the Supreme Court Regulation on Restorative Justice (PERMA RJ), and practices in other countries such as the United States and Canada, it is found that there is an urgency to establish a legal mechanism that gives judges limited authority to conditionally determine closed trials based on the consent of the parties and the principle of confidentiality. This reconstruction aims to realize a criminal justice system that is fairer, more participatory, and based on the humanitarian values of Pancasila and the mandate of the constitution.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.769
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0050.003
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.109
GPT teacher head0.393
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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