Clues to fostering a program culture of academic integrity: findings from a multidimensional regression model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Using multivariate regression, we identified situational, personal and contextual variables correlated with business students’ self-reported rates of academic misconduct. The most influential predictors of increasing academic misconduct were: higher estimates of peers’ academic misconduct, 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 a culture of academic integrity a priori. Finally, we recommend a public health practice of identifying positive deviants – individuals who thrive in challenging environments – and then in an effort to change a peer support system that fosters academic misconduct into one that discourages it, engaging with those individuals to understand why and how they resist the status quo.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| 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 it