PROPOSING RESTORATIVE JUSTICE MODELS AS ALTERNATIVE APPROACHES TO ADDRESSING CRIMINAL MATTERS: A CASE STUDY OF JUDICIAL SYSTEMS IN CIVIL AND COMMON LAW COUNTRIES
Bibliographic record
Abstract
Background: In recent years, restorative justice has emerged as a mechanism to enhance the involvement of victims in criminal proceedings. Its primary objective is to repair the damage caused by the offence, acknowledging it as a genuine injury in need of healing. While criminal proceedings might vary across jurisdictions based on fundamental principles of human rights, the broader aim is to offer a more comprehensive response to crime, aiming not only to punish but also to reform offenders and reduce future criminal behaviour. Methods: This qualitative study employed a descriptive, analytical method, utilising case studies and comparative analysis to explore restorative justice models in established judicial systems and their applicability to unestablished framework countries. By analysing and synchronising secondary materials, the research aimed to provide in-depth insights into successful practices and potential adaptations. Results and conclusions: The results reveal that several restorative justice models have been developed all over the world to align with the legal, socio-political, and cultural contexts of different regions and jurisdictions, such as Canada, New Zealand, and Norway. Despite the variety of restorative justice models, this exploratory study scrutinised four non-adversarial decision-making models: victim-offender mediation, community reparative boards, family group conferencing, and circle sentencing. These four models illustrate an alternative approach to community involvement in crime response, emphasising the diversity and shared themes of community engagement in sanctioning processes. The results offer resourceful guidelines for unestablished judicial systems like Vietnam to choose models best suited to specific needs.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".