An exploration of the role of Canadian academic libraries in promoting academic integrity
Bibliographic record
Abstract
The incidence of student plagiarism at Canadian universities and colleges is cause for concern. As a result, Canadian universities are increasingly using text-matching software such as Turnitin to address the problem of cut-and-paste plagiarism. An exploratory survey was conducted at Canadian research-intensive universities subscribing to Turnitin to examine the role of librarians in educating students and faculty about academic integrity. Results indicate that librarians at these institutions are actively involved in promoting academic integrity and deterring plagiarism. At most institutions surveyed, discussions of academic integrity and the ethical use of information are included in library workshops and library instructional materials. These results, while preliminary, are an important first step to encourage libraries to consider their role within universities for promoting academic integrity. This is a revised version of a paper presented at LILAC 2005: Librarians Information Literacy Annual Conference, Imperial College, London, April 4-6, 2005.
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.034 | 0.006 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".