An Exploration of Faculty Attitudes Toward Student Academic Dishonesty in Selected Canadian Universities
Bibliographic record
Abstract
This work explores faculty attitudes towards student academic dishonesty in Canada by means of a qualitative review of seventeen selected universities’ academic dishonesty policies combined with a quantitative survey of faculty attitudes and behaviors around academic integrity and dishonesty. The data is integrated in the interpretation phase to give depth and breadth to the analysis. The study found that a majority of the faculty members who responded to the survey believe that academic dishonesty is a problem at their institutions and is a problem that is getting worse. Generally, faculty members believe their respective institutional policies are sound in principle but fail in application. Two of the major factors identified by faculty members as contributing to academic dishonesty are administrative. Many faculty members report reluctance to formally report academic dishonesty due to excessive burdens of paperwork and proof. Further, they feel unsupported by administration. Two other major factors contributing to a rise in academic dishonesty are related to students. Faculty members in this study cite unprepared students and international students who struggle with language issues and with the differences between the Canadian academic context and that of their home countries as major contributors to academic dishonesty. This study concludes with a number of recommendations for educators and recommendations for future research.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.024 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".