Restorative Practices as a Tool to Affect Peer Influence on Academic Integrity and Misconduct
Bibliographic record
Abstract
Integrity is often related to acting ethically based on intrinsic motivation, rather than external controls. The disconcerting spike in misconduct cases in the pandemic-related, uninvigilated, remote learning environments has made it clear how far away from promoting integrity and its fundamental values (ICAI, 2021) over mere rule compliance we really are. Further, research routinely finds peer influence to be one of the most important factors affecting academic misconduct. Restorative Practices (RP), when applied not only in response to misconduct, but also as an educational and proactive community building tool, have been shown to be an effective way of preventing misconduct by fostering a sense of trust and community. They have further been found to empower marginalized individuals and communities, pursue and demonstrate fairness, as well as foster empathy, compassion, and accountability. Moreover, RP provide a process that enables student engagement and thus an opportunity to engage in peer-to-peer interaction on the topic of academic integrity and misconduct. In our presentation, we will provide a brief introduction to the principles of RP, followed by a discussion of their application to academic integrity and misconduct at MacEwan University, with a particular focus on student engagement and peer to peer interaction.
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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 | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.022 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".