Infusing Equity, Diversity and Inclusion (EDI) into Academic Integrity Practices in Canadian Higher Education
Bibliographic record
Abstract
Based on our experiences at four Canadian institutions of higher education, we contend that infusing EDI-informed language within academic integrity policy and procedures is important and should be supported by: (a) a transformative approach towards academic integrity that shifts from a “morality and rule compliance” framework (Penaluna & Ross, 2022); (b) asking questions such as, “what do we as instructors and institutions need to unlearn?” (McNeill, 2022) to cultivate belongingness and learning together about diverse systems and cultures of knowledge making (Davis, 2022); and (c) training students, staff, and instructors about ways to highlight aspirational aspects of integrity as well as diminishing anxiety ridden misconduct processes. Thus, to balance the maintenance of rigorous academic standards against the development of a more learning-centred culture of academic integrity, we believe EDI-informed best practices should be established at a system-level across multiple stakeholders responsible for different learning contexts. As a roadmap for structuring educative opportunities for students in such multiple teaching and learning contexts, we consider sites where revised practices might be most impactful, including: i) instructor-led classroom teaching; ii) administrator-led decision making and disciplinary processes; and iii) staff-led and student-centred programming, such as orientation, peer mentoring and learning services sessions.
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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 | 0.052 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.115 | 0.084 |
| Scholarly communication | 0.025 | 0.010 |
| Open science | 0.007 | 0.037 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 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".