Interinstitutional perspectives on contract cheating: a qualitative narrative exploration from Canada
Bibliographic record
Abstract
Abstract This paper explores contract cheating from the perspectives of researchers at three post-secondary institutions in Alberta, Canada, describing their efforts to develop and advance awareness of, interventions against, and responses to contract cheating at their respective institutions. Contract cheating is when a third party produces or completes academic work for a student, and the student then presents the work as their own. The student might have personal connections to the third party, or the student might pay a fee and outsource the academic work to the third party. All three institutions are experiencing an increase in the incidence of contract cheating, which is consistent with trends at colleges and universities across Canada and the world. Contract cheating is not a new phenomenon, but it is a growing one, due in part to students having access to thousands of online companies offering to help them with their academic work. This paper examines personal narratives from four researchers and identifies five key themes: types of contract cheating, students, awareness, evidence and policy implications, and educational development.
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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 | 0.021 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.053 | 0.042 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".