Clues to Fostering a Program Culture of Academic Integrity: Findings from a Multidimensional Model
Bibliographic record
Abstract
Drawing on the responses from a survey of 852 undergraduates in a business program in Canada we identified situational, personality and contextual variables correlated with business students’ self-reported rates of academic integrity violations. The most influential predictors of increasing rates were: greater estimates of peers’ violations, increasingly negative perceptions of the program’s academic integrity culture, and rating questionable academic behaviours less seriously. Individual priorities, personal characteristics and social support were less influential. We then analyzed our quantitative results in light of our deep understanding of the broader context in which the students were located to derive richer insights from the interplay of our independent variables. Importantly, our results indicated that program-led proactive messaging designed to foster a culture of academic integrity could effectively buffer tendencies towards academic dishonesty. Absent ongoing messaging, however, increasing academic pressures may have eroded those initial benefits. Moreover, we identified how repercussions of major academic integrity breaches could be long lasting suggesting an even greater need for fostering academic integrity culture a priori. Based on our results we recommended a public health practice of identifying positive deviants – individuals who thrive in hostile environments – and then, in an effort to change a peer support system that fostered increasing rates of violations into one that does the opposite, engaging with those individuals to understand why and how they resisted the status quo.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.004 | 0.020 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".