Bibliographic record
Abstract
Alarming numbers published in academia and in the media produce the perception that plagiarism is a widespread and urgent problem (e.g., Briggs, 2009). This project explores the potential extent of accidental plagiarism by assessing Canadian distance education students’ knowledge of the concept. Four pieces of evidence are analyzed: (1) students’ attempts to select plagiarised passages from a number of choices; (2) paraphrases these students produced; (3) results from a simple exercise aiming to improve plagiarism understanding; (4) the types of errors made in identifying and writing paraphrases. \n \nTwo different groups of university students were asked to recognize plagiarised work in which wording from the original had been changed in various ways. Students from the online Psychology course received feedback on their recognition attempts and then were asked to paraphrase a passage. The prediction is that with feedback and practice, this group should improve over time. A second group of more diverse students was tested to see if the results generalize. For the second group, undergraduate and graduate students were selected from throughout the university rather than from a single course. All four multiple choice scenarios included a proper citation. \n \nThis study found that almost half of the students in a third-year psychology course did not recognize plagiarised material consistently. The evidence does not support the prediction that student scores would improve over time given feedback and practice, as more students got the first question correct than the fourth question. Furthermore, the majority of these students did not correctly paraphrase a passage they were asked to write in their own words, even after they had received feedback on their recognition quizzes. This suggests more extensive instruction is needed. \n \nUndergraduate and graduate students from throughout the university also failed to recognize many plagiarised passages that included word strings, reversals, substitutions, additions, and deletions. The poor ability of students to identify plagiarised passages may imply poor understanding of the concept (Hochstein et al., 2008). Therefore, when these students write their course essays, they may not be able to recognize their own tendency toward plagiarism and thus engage in it accidentally. \n \nRather than perceiving plagiarism as a type of cheating, it may be more appropriate to identify it, particularly poor paraphrasing, as a weakness in skills. The remedy for committing plagiarism should be sending students to tutorials or other methods of learning to read, write, and reference at the level required for the discipline (Briggs, 2009). \n \nReferences available from author.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".