Visual Plagiarism: Seeing the Forest and the Trees
Bibliographic record
Abstract
Abstract Recent years have seen an increase in conversations in higher education around academic integrity. The subject of plagiarism in traditional written assessments has been much discussed and well researched. Considerably less is known about visual plagiarism. For the purposes of this chapter, we are defining “visuals” as mechanisms that convey meaning without articulation of, or dependence on language. Although some scholarly literature on visual plagiarism exists, there is a dearth of comprehensive literature on the topic and even less published are instructional or best-practice resources for instructors. Further complicating this topic are the differing ethical, legal, professional, and academic standards across fields. Here, we discuss practical ways to pre-emptively approach the topic of visual plagiarism through the education of faculty and students. We address prevention with suggestions for best practices in four distinct disciplines. Additionally, academic policy and administrative challenges are explored. Finally, we make recommendations for further research. This chapter will be of use both across Canada and globally, by providing a framework for defining and examining visual plagiarism in academic contexts and offering guidelines for pedagogical approaches to educate faculty, administration, and students on this important issue.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".