The Juvenile Justice Programs: An Institutional Ethnography Study of the Rules and Procedures Crisis in Contemporary Korean Court System
Bibliographic record
Abstract
From the standpoint of everyday experience, this paper reveals how the rules and procedures used within the Criminal Justice System in Korea to assess juvenile crime disproportionately favors higher income individuals. Specifically, it identifies the importance of how people are represented and classified within the criminal justice system in the application of restorative justice programming. Drawing on a methodology of Institutional Ethnography, texts related to the implementation of restorative justice within the Korean Criminal Justice System were reviewed, and interviews were conducted with key individual(s) charged with implementing restorative justice practices. Findings from this research show a clash between the Korean cultural ethics of “jeong,” which stresses the need for restoration and peace in the face of all criminal actions, and the selective application of restorative justice in the modern Korean context. In particular, the rules and procedures used in the classification of juvenile offenders limits what types of individuals and communities are allowed to benefit from these programs. This is problematic, as delinquents receiving the benefit of restorative justice programing will only be limited to those classified as being 'compatible' with existing programming under the Criminal Justice System's rules and procedures, significantly disadvantaging lower income juveniles.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".