Bibliographic record
Abstract
Abstract Prior researchers have used surveys to identify frequencies and types of academic integrity violations among students and to identify factors correlated with academically dishonest behaviours. Some studies have also explored students’ justifications for their behaviors. Comparatively little work, however, has explored students’ opinions on academic integrity using more nuanced and conversational, but still rigorous, methodologies. To address this gap in the literature, we gathered written and oral comments from 44 Canadian undergraduate business students who participated in one of four year-specific computer-facilitated focus groups. Specifically, we analyzed students’ responses to questions about the general attitudes among themselves and their peers with respect to academic integrity. We also analyzed students’ suggestions of steps that both they and faculty could take to improve the culture of academic integrity in their program. Our contributions to the field of academic integrity were three-fold. First, we gave voice to students in an area in which historically their opinions had been lacking, namely in the generation of specific actions that students and faculty can take to improve academic integrity. Second, we connected students’ opinions and suggestions to the broader literature on academic integrity, classroom pedagogy, and organizational culture to interpret our findings. Third, we introduced readers to an uncommon methodology, computer-facilitated focus groups, which is well suited to gathering rich and diverse insights on sensitive topics.
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.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".