Bibliographic record
Abstract
Objective – To describe the development and implementation of two courses designed to help university students avoid plagiarism. 
 
 Design – Quantitative and qualitative analysis.
 
 Setting – A university in the United Kingdom.
 
 Subjects – An unknown number of university students who took a Plagiarism Awareness Program (PAP) course between 2008 and 2011, and approximately 3,000 university students enrolled in a Plagiarism Avoidance for New Students (PANS) course delivered via a virtual learning environment (VLE) between October and December 2012. The authors attempted to collect rates of continued plagiarism among students who had taken plagiarism education courses. The authors also surveyed 702 university students about plagiarism in 2011.
 
 Methods – Data collected from PAP participants informed revision of the authors’ approach to plagiarism education and led to development of the second course, PANS. At the end of the course, students completed a test of their knowledge about plagiarism. Authors compared scores from students who took a course supervised by a librarian to the scores from students who took the course independently.
 
 Main Results – Students reported that many aspects of citation and attribution are challenging (p. 149). The authors discovered that 93% of students who completed the PANS course facilitated by a librarian in-person passed the final exam with a grade of 70% or higher, while 85% of students who took the same course independently, without a librarian instructor, in an online VLE scored 70% or higher (p. 155). The authors report that referrals of students who plagiarized declined significantly (p-value < 0.001) since the implementation of a plagiarism avoidance curriculum.
 
 Conclusion – As reported by the authors, first-year university students require more extensive education about plagiarism avoidance. A university plagiarism avoidance program instructed by librarians reduces the total number of students caught plagiarizing and mitigates the need for punitive plagiarism education programs. In discussing the challenges and implementation of plagiarism awareness curricula, the authors contribute to the dialogue about effective approaches to addressing this critical issue in higher education.
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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: no | Not applicable | low |
| gpt | Research integrity Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.156 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".