From plagiarism to progress: Assessing remote delivery of a post-discipline academic integrity intervention
Bibliographic record
Abstract
This paper explores the impact of the [redacted]’s Post-Discipline Educational (PDE) program: an institution-wide skills-based academic integrity intervention for supporting students following a finding of academic misconduct. This collaborative and holistic program launched in 2018, and provides tailored educational support from librarians, writing instructors, tutors, and academic integrity staff. While anecdotal evidence supports this educational programming, prior to this study no formal evaluation at our institution had examined its impact. This evaluation took place during the COVID-19 pandemic, creating an opportunity to gain insight on the impact of transitioning to an online learning environment in cases of academic misconduct. Results indicated that the PDE program positively affected students’ intention to seek supports related to their academic and non-academic needs, and increased students’ confidence in their academic skills and ability to avoid future academic misconduct. Findings also suggest that the pandemic may have been an influencing factor in students’ misconduct allegations and their ability to get back on track.
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How this classification was reachedexpand
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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.015 | 0.068 |
| 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; both teacher heads agree on what is shown here.
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".