Toward an alternative community-driven restorative model of school discipline
Bibliographic record
Abstract
Introduction -- Education is concomitantly a path for self-actualization and personal growth, a mechanism of social mobility, a crucial factor of labour market outcomes and lifetime earnings, a social determinant of health and quality of life, a fundamental institution in a democratic society, and a human right. Because education is so consequential at the individual and societal levels, barriers to it are a cause of great concern for various stakeholders, including parents, students, and community advocates. As a case in point, several advocates have identified issues pertaining to school discipline processes and outcomes in Nova Scotia (Barjun Consultants, 2001; Black Learners Advisory Committee, 1994; Nunn, 2006). Recently, we learned that Black learners in Nova Scotia are more likely to be subjected to out-of-school suspensions than other students across the province (Woodbury, 2016). Yet, scholarly literature indicates that punitive school discipline, such as out-of-school suspensions, are ineffective, worsen problematic behaviour, alienate students from school, and keep students away from formal learning opportunities (Fenning et al., 2004; Gregory et al., 2010; Jean-Pierre & Parris-Drummond, 2018; Mayer, 2001; Noguera, 2003). <div><br></div><div>This report is based on the main findings of a qualitative study that aimed to address school discipline racial disproportionality in Nova Scotia, conceive an alternative model of school discipline, and promote the academic success of Black learners. A research project was conducted with African Nova Scotian and Black immigrant youth, parents, and community members across the province between 2018 and 2019 in both English and French. Through nine focus groups and 17 interviews, 60 members of the community provided their perspectives and insights to inform the conception of an alternative model of school discipline. A community forum subsequently took place in March 2020 to discuss the findings and main recommendations with community members. Based on the research project and scholarly literature, this report presents key findings and recommendations that can foreground future school discipline policy and interventions to further Black learners’ academic success in Nova Scotia. Overall, the key themes derived from participants’ narratives emphasize the need to adopt alternative non-punitive responses to misconduct—primarily a restorative model—while simultaneously enhancing inclusive curricular and pedagogical practices to foster Black students’ academic success.</div>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".