The Ethics of Outsourcing: Contract Cheating in Medicine and Health Sciences
Bibliographic record
Abstract
The use of essay mills and other online sites to buy academic work is a global industry estimated to be in the hundreds of millions of dollars. The practice is known as “contract cheating” (Clarke & Lancaster, 2006), and it occurs when a student engages with a third party to complete academic work on their behalf. Canada lags behind other nations in terms of research and awareness about what contract cheating is, how extensive the industry is and the implications for students, institutions and society, but we are beginning to make advances in scholarship, awareness and advocacy. In this presentation, I synthesize available evidence about contract cheating, highlighting what is known about this form of misconduct in medicine and health sciences. Keywords: cheating, academic integrity, academic misconduct, academic dishonesty, contract cheating, essay mills, medical education, health, medicine, Canada Additional material: 6 figures, 1 table, 23 references.
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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 | 0.026 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.021 | 0.051 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 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".