Restorative justice in schools: reviewing best practices and implementation strategies
Bibliographic record
Abstract
In recent years, school districts have taken an interest in restorative justice as\nthey consider alternative ways of securing school safety. This has become a priority for\nschools, communities, and governments in light of several incidents of extreme violence\nin Canada and the United States. These incidents have received a lot of attention and\nforced schools to examine alternative ways of preventing and responding to bullying,\nviolence, and disruptive behaviour (Morrison, 2007).\n\nThis paper is a review of the related literature regarding restorative justice and its\nimplementation in schools. There is little available research in this area from a number of\ncountries including Canada. There is a trend to move toward restorative justice in the\nschool system and conducting this review will be an asset to schools considering change\nas well as stakeholders in order to have the opportunity to review what has worked or not\nin other school districts so they can make informed decisions. The importance of having\nthe school board, administrators, and principals support to lead the change along with the\ndevelopment of a whole school approach to solving problems has been echoed\nthroughout the literature. Implementing restorative justice in schools can be challenging\nas it requires the whole school to shift their thinking toward the development of a\ncommunity and changing the culture within the school. To be most effective, restorative\njustice practices should be implemented in elementary schools so that restorative values\nand practices can be taught to younger children. There must be some process\nimplemented to assist school staff to be directly involved in restorative practices while\nstill fulfilling their primary responsibilities, the education and training of all school staff\nin restorative practices must be focused and continual, and funding and other resource support for the process, for a follow-up period, and for empirically-based evaluations\nmust be secured prior to implementing restorative practices in schools. Related to this,\nand critically, the objectives and measures used to identify success and failure must be\nclearly established in ways that both achieve the specific needs of the school community,\nbut also serve to allow the school to be evaluated and compared to other schools that have\nand have not employed restorative practices.
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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 | 0.045 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".