A model for preventing academic misconduct: evidence from a large-scale intervention
Bibliographic record
Abstract
Abstract It is well known that students intentionally and unintentionally commit academic misconduct, but how can universities prevent academic misconduct and foster a culture of academic integrity? Based on a literature synthesis, an actionable Model for Preventing Academic Misconduct is presented. The model’s basic premise is that students’ voluntary participation in individual courses or academic integrity modules will have far less impact on preventing academic misconduct than required faculty or university-wide programming in core courses. In validating the model, the steps taken by the School of Business at a Canadian university to prevent academic misconduct are examined. Two online tutorials were created and implemented as required modules in the School of Business introductory core courses. Actual academic misconduct incidents recorded by the University from 2016 to 2021, a three-year pre-intervention period and a two-year post-intervention period partly covering the COVID-19 outbreak, are used to gauge the model’s effectiveness in preventing academic misconduct. The findings are discussed through a Social Learning Theory lens: the high-level implementation gives rise to a culture of academic integrity propelled by the establishment of common knowledge.
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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 | 0.037 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".