Justifying academic dishonesty: A survey of Canadian university students
Bibliographic record
Abstract
Academic dishonesty is a growing problem in the higher education sector. Using a sample of 321 undergraduate students at a medium-sized Canadian university, this paper explores the respondents’ acceptability of the various reasons for engagement in academically dishonest behaviour. The findings revealed that respondents displayed moderately negative attitudes toward academic dishonesty and that the top three circumstances under which academically dishonest behaviour would be considered acceptable were pressure to maintain a scholarship, pressure from parents to perform well, and the heavy academic work load. Multiple ordinary least-squares regression analysis revealed that male respondents and those who reported a higher family income, enrolled in more classes, witnessed academic misconduct more frequently, expressed dissatisfaction with academic performance, indicated dissatisfaction with school life, placed less emphasis on intrinsic motivation to pursue higher education, and adopted a surface approach to learning were found to be associated with a greater likelihood of accepting the various justifications for academic dishonesty. The results of this investigation may be utilized by university administrators, academic advisors, and academic counselors to aid in the design of support services and interventions (e.g., explicit guidelines and practical teaching/learning resources) that will serve to prevent academic misconduct and to promote academic integrity.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".