Building a Culture of Restorative Practice and Restorative Responses to Academic Misconduct
Bibliographic record
Abstract
Abstract TheUniversal Declaration of Human Rights’Article 26 on education and more recently UNESCO’s “World Declaration on Higher Education for the Twenty-First Century: Mission and Action” have called for civic and ethical education alongside academic education in postsecondary settings. Many post-secondary institutions have made fostering civic responsibility, engaged citizenship, and ethical decision making in students a strategic priority. What often remains ambiguous is how these priorities translate into action. A growing body of scholarly literature and research establishes the role Restorative Practice (RP) can play in pursuing these strategic priorities surrounding moral development, emotional intelligence, and engaged citizenship. Specifically, RP has been shown to prevent conflict and misconduct, while empowering marginalized individuals. Restorative practices demonstrate fairness; and foster empathy, compassion and accountability; through experiential learning opportunities. In light of these developments, MacEwan University, in Alberta, Canada, has been actively building a restorative culture. One aspect of this endeavour was the revision of its Academic Integrity Policy and Academic Misconduct Procedures to include the possibility of alternative resolutions to academic misconduct, based on restorative practices and principles. In our chapter, we will (a) provide a brief introduction to restorative practices that makes explicit its connection to universities’ civic education mandate, integrity, and specifically, academic integrity; (b) describe the restorative practices model that is being established at MacEwan University; (c) discuss in detail the application of restorative practices to academic misconduct cases, including training of facilitators, as well as successes and challenges experienced in the first year since it became available; and, finally, (d) share feedback regarding its effectiveness received from students, staff, and faculty who participated in restorative resolutions.
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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 | 0.034 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.028 | 0.058 |
| Scholarly communication | 0.020 | 0.010 |
| Open science | 0.005 | 0.025 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".