Practical approaches: decolonizing academic integrity
Bibliographic record
Abstract
Looking through a critical transformative lens at the current practices and policies in post-secondary institutions regarding academic integrity and the perceptions of academic integrity in these practices, there emerged an understanding of the ways in which these practices and policies at times contribute to negative academic integrity outcomes in these same institutions. Specifically, this study examines how the current punitive and legalistic perception of academic integrity within colonialist and capitalist structures can lead to inequitable outcomes and harm for both learners and educators and offers a means to begin to address these issues of inequity. The literature review describes the historical context of academic integrity in Canada and takes a critical look at this history, followed by literature on the decolonization of academic integrity and the possible benefits offered by taking decolonized and restorative practice approaches to academic integrity. These approaches were explored as possible avenues or solutions put forward to avoid perpetuating harms that exist in current practices and will be summarized in this project, featuring research-based recommendations for faculty, administrators, and institutional leadership on how to apply decolonization methods and changes to various levels of the academy. This project and its resources fill a current gap in research by emphasizing and making this knowledge accessible, actionable, and adaptable.
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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 | 0.024 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.025 | 0.056 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".