Changing “Hearts” and Minds: Pedagogical and Institutional Practices to Foster Academic Integrity
Bibliographic record
Abstract
Abstract This chapter shares findings of and recommendations from a three-year initiative at the University of British Columbia to develop and assess enhanced and explicit instruction in academic integrity in first-year writing courses, an enterprise that now involves 42 faculty members teaching up to 5000 students each year. This project began from the appreciation that, as an institution, we needed to close the gap between our expectations of academic integrity and students’ understanding of those expectations, and to make explicit what is often treated as assumed understanding. This approach was intended to help students develop more robust knowledge and appreciation for academic integrity as a core element of the academic community to which they now belong. Drawing on the qualitative and quantitative data we gathered from students and faculty, including surveys, focus groups, misconduct reports, and interviews, I illustrate how what I call “pedagogies of integrity” have led to improved uptake by students (and instructors) of academic integrity as both theory and practice, resulting in a change in the number as well as type of academic misconduct cases, and have led to significant insights about the place of academic integrity in larger conversations about student belonging, wellness, and access. I share not only how the instructors in this project changed the conversation in their own courses, but also how these discussions are resonating across disciplines and faculties of our campus and beyond. Finally, I outline recommendations for next steps in policy and practice that these findings suggest.
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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 | 0.043 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.039 | 0.050 |
| Scholarly communication | 0.020 | 0.011 |
| Open science | 0.005 | 0.029 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".