Clues to Fostering a Program Culture of Academic Integrity: Findings from a Multidimensional Model
Bibliographic record
Abstract
Using zero-inflated negative binomial regression 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 to derive richer insights from the interplay of our independent variables. Importantly, our results indicate that program-led proactive messaging designed to foster a culture of academic integrity can effectively buffer tendencies towards academic dishonesty. Absent ongoing messaging, however, increasing academic pressures may erode those initial benefits. Moreover, repercussions of major academic integrity breaches can be long lasting suggesting an even greater need for fostering academic integrity culture a priori. Finally, we recommend 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 fosters increasing rates of violations into one that does the opposite, engaging with those individuals to understand why and how they resist the status quo.
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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 | 0.018 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".