Relational Restorative Justice Pedagogy in Educator Professional Development
Bibliographic record
Abstract
What would a professional development experience rooted in the philosophy, principles, and practices of restorative justice look and feel like? This article describes how such a professional development project was designed to implement restorative justice principles and practices into schools in a proactive, relational and sustainable manner by using a comprehensive dialogic, democratic peacebuilding pedagogy. The initiative embodied a broad, transformative approach to restorative justice, grounded in participating educators’ identifying, articulating and applying personal core values. This professional development focused on diverse educators, their relationships, and conceptual understandings, rather than on narrow techniques for enhancing student understanding or changing student behaviour. Its core practice involved facilitated critical reflexive dialogue in a circle, organized around recognizing the impact of participants’ interactions on others, using three central, recurring questions: Am I honouring? Am I measuring? What message am I sending? Situated in the context of relational theory (Llewellyn, 2012), this restorative professional development approach addresses some of the challenges in implementing and sustaining transformative citizenship and peacebuilding pedagogies in schools. A pedagogical portrait of the rationale, design, and facilitation experience illustrates the theories, practices, and insights of the initiative, called Relationships First: Implementing Restorative Justice From the Ground Up.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.024 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".