Are Canadian professors teaching the skills and knowledge students need to prevent plagiarism?
Bibliographic record
Abstract
Abstract Max 150 words. If possible, please submit your abstract in both English and French. When writing an assignment, most students start by searching for information online, which they integrate in their writing and conclude by producing a bibliography for the sources used. They use their informational, writing and referencing skills to do this as well as refer to their plagiarism knowledge to make sure their text is exempt from plagiarism. In this paper, we examined which skills and knowledge students feel the need to further develop in university to prevent plagiarism in their assignments. Professors were also questioned as to their perceptions of their students’ skills development during their pre-university studies. Questionnaires were administered in six Quebec Universities to students ( n = 1170) and professors ( n = 279). Results show that students feel the need for more training while professors expect students to have already mastered the skills and knowledge to prevent plagiarism. Recommendations are made on how to implement better training for students through a program approach. Lors de la rédaction d’un devoir, la plupart des étudiants universitaires commencent par chercher des informations en ligne, qu’ils intègrent dans leur rédaction et terminent en produisant une bibliographie des sources utilisées. Ils utilisent leurs compétences informationnelles, rédactionnelles, et de référencement documentaire et se réfèrent à leurs connaissances en matière de plagiat pour s’assurer que leur texte en soit exempt. Dans cet article, nous avons examiné les compétences et les connaissances que les étudiants ressentent le besoin de développer davantage à l’université pour prévenir le plagiat dans leurs travaux. Les professeurs ont également été interrogés sur leur perception du développement des compétences de leurs étudiants durant leurs études pré-universitaires. Des questionnaires ont été administrés dans six universités québécoises à des étudiants ( n = 1170) et à des professeurs ( n = 279). Les résultats montrent que les étudiants ressentent le besoin d’une formation plus poussée alors que les professeurs s’attendent à ce que les étudiants maîtrisent déjà les compétences et les connaissances nécessaires pour prévenir le plagiat. Des recommandations sont formulées sur la façon de mettre en œuvre une meilleure formation pour les étudiants par le biais d’une approche-programme.
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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: yes · About a Canadian topic: yes | Observational | low |
| gpt | MetaresearchResearch integrity Domain: Methods · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Observational | high |
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.002 |
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".