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Record W4406626464 · doi:10.31289/jiph.v11i2.10837

Penerapan Restorative Justice di Negara Amerika Serikat

2024· article· en· W4406626464 on OpenAlexaboutno aff
Dewi Wahyuningsih

Bibliographic record

VenueJurnal Ilmiah Penegakan Hukum · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsRestorative justicePolitical scienceLaw

Abstract

fetched live from OpenAlex

Restorative justice has been part of the criminal justice system in the United States for more than three decades. Beginning in the 1970s, this approach emerged as an alternative to the retributive nature of the justice system, which tends to focus on punishment as a response to violations of social norms. In contrast, restorative justice places crime victims at the forefront by encouraging dialogue, reconciliation, and the restoration of relationships between offenders, victims, and communities. This approach aims to create a more holistic resolution and reduce the likelihood of reoffending. This study reviews how Victim Offender Mediation (VOM) serves as one of the main paradigms in the implementation of restorative justice in the United States. VOM allows victims and offenders to engage directly in conflict resolution, resulting in mutually agreeable solutions. In addition, this study compares restorative justice policies in the United States with similar measures in Canada, including the passage of the Youth Criminal Justice Act (2002) which reflects the principles of restorative justice. The results show that restorative justice has the potential to be a more humane and effective approach in the criminal justice system. However, consistent implementation and a supportive legal framework are needed to ensure its sustainability. This study provides important insights for the development of more inclusive and restorative-oriented criminal justice policies in the United States and other countries.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.669
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.356
Teacher spread0.323 · 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; a candidate call from one teacher head, not a consensus.

Study designNot applicable
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
Published2024
Admission routes1
Has abstractyes

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