Bibliographic record
Abstract
Purpose The paper aims to discuss the ways in which librarians of different types are addressing the issue of plagiarism at the institutional and pedagogical levels. Design/methodology/approach A 25‐question non‐quantitative online survey was conducted regarding: the institutional role of librarians in plagiarism prevention; the collaborations among librarians and instructors in helping students understand what plagiarism is and how to avoid it; and the interactions among librarians and students involved in combating plagiarism. Findings More than 90 percent of the 610 respondents report that they have assisted students with citing sources. Over 70 percent have instructed students about plagiarism in class. Approximately a quarter have collaborated with other departments regarding plagiarism, conducted or attended workshops on plagiarism, worked with instructors to redesign assignments, or helped faculty with tracking possible instances of student plagiarism. Research limitations/implications This paper reports on a survey which is not statistically valid. The results of this survey, however, can shed light on the librarian's role to date in combating plagiarism and suggest future directions. Practical implications This survey reports what librarians are doing to address plagiarism at all levels, and it reflects what is being practiced in the field. Originality/value While many librarians have written about plagiarism strategies, this national survey focuses on the work of librarians at the institutional and pedagogical levels.
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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 | 0.045 | 0.128 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".