Bibliographic record
Abstract
Academic integrity and academic misconduct are issues that affect all post-secondary institutions, and the University of Manitoba has seen a rising number of cases (Annual Report of the University Discipline Committee, 2021). Furthermore, there has been a call (Bertram Gallant, 2008; Sopcak & Hood, 2022) for developing and implementing responsive educational approaches in academic misconduct cases at the post-secondary level. These educational approaches move away from quasi-legal and punitive measures, which have been shown to negatively impact the well-being of the involved students (Pitts et al., 2020). Instead, it has been argued that academic misconduct should be addressed as a learning, teaching and skills development concern. In this presentation, we discuss the implementation of a reflection-based course for students involved in cases of academic misconduct. Completion of this self-directed online course is the preliminary step in educational outcomes for most students involved in academic misconduct at the University of Manitoba. We chart the shift in this educational outcome from a quiz-based tutorial to a multi-module -reflection-based approach. We will discuss the rational and practical implications of the “Reflections on Academic Integrity (RAI)” course. We will argue that the RAI course actively and consciously moves away from the stigmatization associated with academic misconduct towards framing the student’s experiences as a learning outcome that sets the foundation for a return to successful studies.
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How this classification was reachedexpand
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 0.008 |
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; both teacher heads agree on what is shown here.
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".