Revisiting Criminal Justice: From Retribution to Restoration in a Technological Era
Bibliographic record
Abstract
This narrative review explores contemporary challenges and reform trends in comparative criminal justice systems, emphasizing the interaction between retributive and restorative approaches and the integration of digital technologies such as artificial intelligence (AI). The study aimed to identify effective reform strategies and the systemic factors influencing their success. A comprehensive literature search was conducted across Scopus, Google Scholar, and other academic databases using Boolean operators to locate studies published in the last ten years, with inclusion criteria focusing on relevance, methodology, and language. Selected studies included qualitative, quantitative, and mixed-method research examining judicial systems in civil and common law countries. Results reveal that reform efforts are significantly shaped by institutional transparency, civic engagement, and corruption control. Countries like Rwanda and Germany demonstrated progress through inclusive reforms, whereas developing nations such as Indonesia face obstacles due to institutional limitations. Retributive models, particularly in the United States, contribute to high recidivism and neglect victims' needs. In contrast, restorative practices in Canada and New Zealand show enhanced outcomes in offender rehabilitation and victim satisfaction. Moreover, the use of AI in judicial systems, while improving efficiency, raises ethical concerns regarding algorithmic fairness and data governance. The findings highlight the urgent need for balanced policy frameworks that promote restorative justice, community engagement, and ethical integration of technology. Future research should examine adaptive models of justice reform suited to varying socio-political environments to enhance justice delivery globally.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".