Plagiarism detection software and academic integrity :\nthe canadian perspective
Bibliographic record
Abstract
In 2003, McGill University, a member of the Canadian “G10” research universities, undertook a limited trial of plagiarism detection software in specific undergraduate courses. While it is estimated that 28 Canadian universities and colleges currently use text-matching software , the McGill trial received considerable attention from student, national and international media after a student refused to submit his work to the service and successfully challenged the university’s policy requiring the use of Turnitin™. \nWhile student and faculty reactions to the software have been mixed, debate about the use of text-matching software has served to promote awareness of the importance of academic integrity and the use of alternative methods of deterring plagiarism. No final decision has yet been reached regarding the use of plagiarism detection software but the University is currently drafting policy for its general implementation in courses and specific use in cases of suspected plagiarism. At the same time, it is working to develop collaborative initiatives involving key campus stakeholders, including the University administration, Teaching and Learning Services, librarians and student advocacy groups, to promote academic integrity at McGill.\nIn this study, we seek to determine how leading Canadian universities using text-matching software address issues of academic integrity. Particular attention will paid to the role of librarians in promoting academic integrity and in educating students and faculty about information literacy. Having identified seven of the G10 currently using Turnitin™, we intend to survey key stakeholders from each institution via electronic questionnaire for information on four areas relating to academic integrity: promotion, policy, education, and library involvement. We expect to report a summary of our findings, paying special attention to the current situation at McGill.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Research integrity Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Not applicable | medium |
| gpt | Research integrity Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: yes | Not applicable | low |
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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.020 |
| Science and technology studies | 0.048 | 0.030 |
| Scholarly communication | 0.029 | 0.012 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".