Bibliographic record
Abstract
In this article, we present early results of surveys conducted at the University of Toronto, Mississauga campus, across four terms (January 2020 to December 2021) of early-year undergraduate students to determine their understanding and views of plagiarism. Our survey instrument gathered basic demographic information as well as asked participants to respond to 24 statements using a 5-point Likert scale. We share our analysis of responses to 15 statements in the survey which were intended to provide an understanding of the “moral universe” of students—that is, the way that they contextualize plagiarism in terms of their moral standards. Our major finding is that although students across three disciplines (Humanities, Social Science, and Science) recognized the potential harm of plagiarism to the value of their degrees, they also believed self-plagiarism to be less serious than other forms of academic integrity offences. We consider how the moral universe of students differs from the moral universe implied in the University’s codes and argue that the messaging used by academic institutions should convey the reasons for taking plagiarism seriously. We argue that presenting plagiarism as similar to theft of property rather than an issue of pedagogy might inadvertently encourage students to consider self-plagiarism to be more acceptable than other forms of plagiarism.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".