Are University Students Armed with the Necessary Skill Level to Prevent Plagiarism?
Bibliographic record
Abstract
When writing their assignments, university students must search for information online and integrate it into their papers without plagiarizing. They must also support their ideas with references according to their university's standards and requirements. Producing a university assignment thus requires students to use informational skills, academic writing skills, and referencing skills throughout the process. Students are admitted into undergraduate programs with varying levels of mastery of these skills. According to one research, Quebec students expect to develop these skills during their university education, while their professors often expect students to have previously acquired them. Is it possible that this chasm in expectations results in plagiarism because students are not sufficiently prepared? Do universities in other countries face the same situation? This is what we aimed to verify. A quantitative data collection was done in universities in Canada, the United States and in Europe. Two online questionnaires were administered to undergraduate students and professors to obtain information on their use and teaching of informational, writing, and referencing skills. For this presentation, the responses to six specific corresponding questions (students vs faculty) were analyzed: expectation of skill development and level of skills. Since tackling plagiarism at an institutional level requires a shared implication, this analysis will help faculty, administrators, librarians, and tutors to understand students’ experience of the required skills to avoid plagiarism.
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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 | Observational | high |
| gpt | MetaresearchResearch integrity Domain: Methods · Genre: Empirical About the Canadian research system: no · 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 | 0.005 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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.
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".