Academic Dishonesty in the Canadian Classroom: Behaviours of a Sample of University Students
Bibliographic record
Abstract
Academic dishonesty is a persistent problem in institutions of higher education, with numerous short- and long-term implications. This study examines undergraduate students’ self-reported engagement in acts of academic dishonesty using data from a sample of 321 participants attending a public university in a western Canadian city during the fall of 2007. Various factors were assessed for their influence on students’ extent of academic dishonesty. More than one-half of respondents engaged in at least one of three types of dishonest behaviours surveyed during their tenure in university. Faculty of enrolment, strategies for learning, perceptions of peers’ cheating and their requests for help, and perceptions and evaluations of academic dishonesty made unique contributions to the prediction of academic dishonesty. High self-efficacy acted as a protective factor that interacted with instrumental motives to study to reduce students’ propensity to engage in dishonest academic behaviours. Implications of these findings for institutional interventions are briefly discussed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".