Effectiveness of Restorative Discipline in Alternative Schools
Bibliographic record
Abstract
Behavior and discipline are major issues in our classrooms and campuses \ntoday. Traditional discipline, behavior management strategies that have been used in the school \nsystems over the centuries, has been connected to the school to prison pipeline; removing \nstudents from the class and the education that they are in need of, allows for students to fall \nfurther into the cracks. According to the ACLU, ???This failure to meet educational needs \nincreases disengagement and dropouts, increasing the risk of later court involvement.??? \n(2018). Teachers are spending too much time on discipline and discipline procedures and not \nenough time teaching students and meeting their academic needs. According to Barbetta, \nNorona, and Bicard in their 2004 study, ???Many teachers face larger class sizes, more students \nwho come from stressful, chaotic homes, and increased diversity in students??? abilities and \ncultures.??? (2005). Traditional discipline practices, including student suspension and expulsion \nadversely impact student achievement and graduation rates by removing students from the \nclassroom, thus impeding their learning. Restorative Discipline and Restorative Justice have \nbeen discussed as an alternative to suspensions. The purpose of Restorative Practices is to \nimprove and repair relationships within a community, in this instance, the school community. \nRestorative Practices has its roots in many ancient religions, especially Aboriginal and Native \nAmerican religions, many studies show that most eastern religions do have a connection to this \nform of justice, as well. ???The roots of restorative justice models also stem from traditional \nAboriginal methods of conflict resolution that rely on community involvement and \nimplementation of holistic solutions. The continued overrepresentation of Aboriginal peoples in \ncorrectional institutions in Canada has led to demands for more (Aboriginal) traditional \napproaches, such as sentencing circles, for Aboriginal offenders,??? according to Canada???s \nEffective Discipline \n5 \n \nDepartment of Justice. This study is analyzing the possible benefits of student engagement and \nattendance with the inclusion of Restorative Practices in Alternative School settings.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".