Proactive not punitive: Approaching academic integrity from an educational perspective
Bibliographic record
Abstract
Our implementation of Turnitin Similarity Software at Saskatchewan Polytechnic strengthened the overall support services for both faculty and students in preventing plagiarism. Using a holistic approach we drew on the collective intelligence of experts from several departments, which resulted in training sessions, curriculum design support and intensive student support. Our research used a variety of methods such as a literature review, quantitative data from user surveys, results from the similarity software and qualitative data from focus groups that center on perceptions of the issue within the program and the perceived benefits of the software. Highlights include the use of Turnitin as a writing improvement tool for students, shifting the mindset of faculty from being punitive to being proactive, writing clear and consistent assignment instructions, embedding student supports, mandatory student and faculty training in Turnitin, academic integrity education, as well as protecting student rights and intellectual freedom.
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 | 0.018 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.029 | 0.048 |
| Scholarly communication | 0.028 | 0.014 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".