Responding To Cyber Risk With Restorative Practices: Perceptions And Experiences Of Canadian Educators
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Restorative practices are gaining traction as alternative approaches to student conflict and harm in schools, potentially surpassing disciplinary methods in effectiveness. In the current article, we contribute to the evolving understanding of restorative practices in schools by examining qualitative responses from educators regarding restorative interventions for online-mediated conflict and harm, including cyberbullying and sexting. Participants include pre-service educators, as well as junior and senior teachers with varying levels of familiarity with restorative practices. Our findings highlight how educators who have implemented these practices largely hold positive perspectives of their effectiveness for resolving cyber conflicts and restoring a positive classroom environment. Educators emphasize the value of meaningful changes in student behaviour and acknowledge the potential of face-to-face mediation in mitigating online harm and promoting digital citizenship, though some educators raise questions about the appropriateness of restorative responses to serious incidents of online-mediated harm. This research offers fresh insights into the challenges and potential of restorative practices in schools, particularly in addressing cyber-based conflicts. We emphasize implementation challenges related to the distinct contexts in which schools operate and the influence of broader societal and systemic factors on the success of restorative practice initiatives.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it