A Study on the Improvement of Laws and Systems for Resolving School Violence
Bibliographic record
Abstract
This study investigates the reasons why school violence in South Korea continues to rise despite repeated legislative revisions and the announcement of a comprehensive government strategy in 2023. Drawing on the author’s direct experience in the field ranging from elementary school-level violence task forces to court-related mediation the research identifies core procedural and structural problems within the existing legal and institutional framework. Particular attention is given to the introduction of the school violence investigator system, which, despite aiming to reduce teachers’ burdens, has inadvertently increased legal intervention and parent demand for attorneys. The study compares domestic systems with foreign models from Canada, the UK, and Japan, where restorative practices are school-led. It proposes the mandatory introduction of an internal “conflict mediation committee” within schools to prevent unnecessary escalation and promote reconciliation. Furthermore, it suggests legislative amendments to enhance protections against cyberbullying and misuse of investigation procedures such as retaliatory mutual violence claims. The research ultimately argues for a shift from punitive to restorative responses, emphasizing the educational role of conflict resolution and the importance of long-term psychological support. The findings contribute to school violence scholarship by offering a holistic model combining legal reform, school governance, and community-based intervention.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".