Bibliographic record
Abstract
Abstract In this chapter I present an overview of contract cheating in Canada over half a century, from 1970 to the early 2020s. I offer details about a failed attempt at legislation to make ghostwritten essays and exams illegal in Ontario in 1972. Then, I highlight a 1989 criminal case, noted as being the first of its kind in Canada, and possibly the Commonwealth, in which an essay mill owner and his wife were charged with fraud and conspiracy. The case was dismissed by the judge, leaving the contract cheating industry to flourish, which it has done. I synthesize the scant empirical data available for Canada and offer an educated estimate of the prevalence of contract cheating. Finally, I conclude with a call to action for educators, advocates, and policy makers. I conclude with a call to action for Canadians to take a stronger stance against 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.032 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".