Responding Restoratively to Student Misconduct and Professional Regulation – The Case of Dalhousie Dentistry
Bibliographic record
Abstract
The 2015 restorative justice process at Dalhousie University’s Faculty of Dentistry is a case study that reveals the connection at conceptual and practical levels between restorative justice and responsive regulation as common expressions of relational theory and practice. Their relationship is clearest when, as in this case, issues are understood in their full contexts and circumstances require a widening of the circle of issues and parties. At this scale the complexity of the situation and the need for responsive interventions capable of supporting and sustaining a just relationship is revealed.\nThe incident at Dalhousie University’s Faculty of Dentistry involved a private Facebook group established by male class members containing sexist and hurtful comments about female classmates and others. At first blush the incident might appear one of interpersonal harm ripe for a restorative justice process. However, several of the female students involved insisted the nature of the situation was more complex. The behaviours in question were reflective of, and contributed to, a deeply structured culture of discrimination and oppression within the Faculty and the profession. A responsive approach was required, in their view, to secure lasting change in the climate and culture within the educational and profession spheres. The resulting process moved parties beyond their adversarial relations to shared understanding necessary for transformation of climate and culture.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.089 | 0.024 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.015 | 0.022 |
| Insufficient payload (model declined to judge) | 0.007 | 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".