Faculty Perspectives of Academic Integrity During COVID-19: A Mixed Methods Study of Four Canadian Universities
Bibliographic record
Abstract
Faculty members are crucial partners in promoting academic integrity at Canadian universities, but their needs related to academic integrity are neither well documented nor understood. To address this gap, we developed a mixed methods survey to gather faculty perceptions of facilitators and barriers to using the existing academic integrity procedures, policies, resources, and supports required to promote academic integrity. In this article, we report the data collected from 330 participants at four Canadian universities. Responses pointed to the importance of individual factors, such as duty to promote academic integrity, as well as contextual factors, such as teaching load, class size, class format, availability of teaching assistant support, and consistency of policies and procedures, in supporting or hindering academic integrity. We also situated these results within a micro (individual), meso (departmental), macro (institutional), and mega (community) framework. Results from this study contribute to the growing body of empirical evidence about faculty perspectives on academic integrity in Canadian higher education and can inform the continued development of existing academic integrity supports at universities.
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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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.023 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".