Academic Misconduct in Higher Education: Beyond Student Cheating
Bibliographic record
Abstract
Abstract When people hear the term “academic misconduct”, student cheating often comes to mind. In this chapter we provide a broader perspective, presenting formal definitions of the terms academic integrity and academic misconduct, arguing that such concepts should apply to all members of the academy. Unfortunately, research conducted in the UK and the US suggests that faculty and administrators engage in misconduct and unethical practice, in research as well as other domains. Here we review policy changes in Canada’s approach to dealing with research misconduct, with the aim of strengthening “Canada’s research integrity system” (HAL in Innov Policy Econ, 2009, i). We also present public accounts of academic transgressions by Canadian faculty and administrators, with a primary focus on research misconduct. A query ofRetraction Watchfound 321 retractions involving academics working in Canadian higher education institutions during the years 2010–2020. Articles in the press are then used to further highlight incidents of academic fraud and plagiarism, as well as questionable practices in student supervision, hiring practices, international student recruitment, and inappropriate interpersonal relationships. We conclude by calling for a comprehensive study of academic misconduct by faculty and administrators at Canadian higher education institutions as well as an assessment of how well the changes to Canada’s policies on research misconduct are working, particularly with respect to public disclosure.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.010 | 0.027 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".