Bibliographic record
Abstract
As academic honesty policies are being revised at Canadian universities, in order to balance the maintenance of rigorous standards of academic conduct with principles of anti-racism and equity, diversity and inclusion, data on academic honesty breaches become an important source of information for policymaking and improvement of existing practices. Rather than examine the usual kinds of self-report and student survey data found in the scholarship on academic integrity, this presentation explores what conclusions can be drawn from so-called official data on academic honesty breaches in the largest liberal arts faculty in Canada. Collected over two years during the height of the COVID-19 pandemic in Canada, this faculty-level data highlights how academic dishonesty incidents, although counted as instances of individual student misconduct, point to larger systemic issues in teaching and learning in higher education. When performed under emergency, pandemic conditions, online teaching and learning had the effect of exacerbating poor assessment design and learner disengagement at a time when predatory third parties were offering online contract cheating services, especially to racialized students. While present data reporting relies on making a distinction between domestic and international students, such a categorical difference obscures the ways in which racialized students, irrespective of VISA status, are disproportionately caught in the web of academic honesty processes. If data-driven decision-making will help shape the future of academic integrity policies and practices, then it is imperative that we recognize the limitations embedded in the official data that we currently collect.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.004 | 0.039 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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".