Contract Cheating in Canada: National Policy Analysis Project Update and Results for 2021
Bibliographic record
Abstract
Join us for an in-depth look at how contract cheating is addressed in Canadian higher education policies. In this session we share results synthesized from 80 publicly-funded universities and colleges across Canada, where English is the primary language of instruction. Our results show why Canada is lagging behind in terms of addressing contract cheating pro-actively through policy and procedures. We offer concrete recommendations for improving the ways that Canadian schools can address contract cheating and other breaches of academic integrity through policy and procedures. In this study, regional teams assembled to collect and analyze academic integrity policies from 80 publicly-funded universities and colleges across Canada where English is the primary language of instruction (Western Canadian universities, n = 24; Ontario universities, n = 21; Atlantic Canadian universities, n = 13; Ontario colleges, n = 22). Although the entire study is not yet complete, we now have full or preliminary results to share from 9 Canadian provinces (BC, AB, SK, MB, ON, NB, NS, PE, and NL). In this session we offer the most comprehensive synthesis of the project to date. In our presentation we provide an overview of the project as a whole, show how we have conducted the study (i.e., method), and present our findings at both a regional and national level. Based on our findings, we offer evidence-based recommendations for policy reform for academic integrity in Canadian higher education, concluding with a call to action for policy makers and administrators to take a stronger stance against contract cheating. For more information on this project visit https://osf.io/n9kwt/.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.049 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.013 | 0.028 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".