Bibliographic record
Abstract
While the period of stagnation has continued since restorative justice was introduced to Korea more than 20 years ago, attempts have been made to incorporate the idea of restorative justice ideology in police activities under the name of so-called restorative policing. Moreover, as the police have the right to terminate the investigation under the revised Criminal Procedure Act, it provides an opportunity for the police to actively attempt to intervene in the handling of cases. Under these circumstances, looking at the situation in Australia can help minimize trial and error in Korea. Because Australia is known as the birthplace of the so-called Wagawa model, which has been widely spread to common law countries such as UK, the United States and Canada, becoming a model for restorative policing. This study aims to examine recent trends in restorative policing in Australia and to provide basic data for reference in our institutional design. First, I will introduce the details of the operation of the restorative justice in Australia. Next, the performance and tasks in Australia are reviewed. Based on this, implications are derived such as the preparation of screening criteria for cases, the validation of effectiveness for programs, the consideration of the operator and the idea of a sustainable model.
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 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 | 0.002 | 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 | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".