Promising Practices and Emerging Ideas in Academic Integrity Policy Development
Bibliographic record
Abstract
Recently, Athabasca University canvassed faculty, tutors, and students about their perspectives on academic integrity. Responses to open-ended questions were received from 102 faculty and tutors and 146 students, generating hundreds of comments. The survey asked how Athabasca University could improve its policies concerning issues of academic integrity, about satisfaction with how academic violations were treated, on the role of faculty and tutors in encouraging academic integrity, and on how faculty and tutors handled cases of misconduct. As well, we collected suggestions from faculty, tutors, and students for reducing cheating, increasing academic integrity, and other ideas about academic integrity in general. Using content analysis, we categorized these open-ended replies into similar threads. Five general recommendation groupings were extracted: policy and procedures, compliance and commitment, resources, plagiarism detection software, and other. The proposed presentation will focus on two sets of recommendations: policy and procedures and plagiarism detection software. We believe that our work meets the criteria for the call for papers because we are learning from our faculty, tutors and students and are interested in sharing their insights. Although we conducted the study pre-COVID-19, we think the recommendations apply now as much as they did then, and will continue to be useful into the future.
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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 | 0.142 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.042 | 0.062 |
| Scholarly communication | 0.049 | 0.031 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.014 | 0.019 |
| Insufficient payload (model declined to judge) | 0.007 | 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".