Academic Integrity Policy Analysis of Publicly-Funded Universities in Ontario, Canada: A Focus on Contract Cheating
Bibliographic record
Abstract
In this article we report findings from a review of universities’ academic integrity policies in Ontario, Canada. The research team systematically extracted, reviewed, and evaluated information from policy documents in an effort to understand how these documents described contract cheating in Ontario universities (n = 21). In all, 23 policies were examined for contract cheating language. The elements of access, approach, responsibility, detail, and support were examined and critiqued. Additionally, document type, document title and concept(s), specific contract cheating language, presence of contract cheating definitions and policy principles were reviewed. Findings revealed that none of the universities’ policies met all of the core elements of exemplary policy, were reviewed and revised with less frequency than their college counterparts, lacked language specific to contract cheating, and were more frequently focused on punitive rather than educative approaches. These findings confirm that there is further opportunity for policy development related to the promotion of academic integrity and the prevention of contract cheating.
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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 | 0.079 | 0.213 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.020 | 0.051 |
| Science and technology studies | 0.020 | 0.009 |
| Scholarly communication | 0.017 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".