Addressing Equity in Academic Integrity
Bibliographic record
Abstract
This literature review examines equity issues within academic integrity systems in higher education and explores strategies for fostering an anti-racist and equitable approach to academic integrity practice. The study acknowledges that existing academic integrity policies and practices in Canadian higher education are rooted in white-dominant ideologies propagated by colonial history, leading to a number of barriers for marginalized and racialized students. The research question of this study focuses on identifying equity barriers present in academic integrity systems and exploring existing equity practices applicable to academic integrity policy and practice to address barriers. A qualitative review of relevant literature (n = 27) was conducted, utilizing tertiary and secondary sources, and institutional databases. The findings reveal three key equity barriers prevalent in the literature: ways of knowing and belonging, language use, and citations and attribution systems. The study also identifies seven equity strategies from the literature and proposes specific application of these across policy development, academic integrity practice, teaching practice and advocacy work. In conclusion, the literature review highlights the need to address systemic barriers in academic integrity and emphasizes the importance of anti-racist and equitable approaches. By implementing strategies that promote inclusivity, cultural responsiveness, and recognition of diverse knowledge systems, higher education institutions can foster a more equitable academic environment. The review provides relevant recommendations for discussion and advocacy among scholars and academic integrity practitioners.
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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 | 0.019 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".