Exploring Factors Contributing to Plagiarism as Students Enter STEM Higher Education Classrooms
Bibliographic record
Abstract
Students often come to college with a limited understanding of how to ethically incorporate and cite source materials in their writing, and this is commonly cited as the leading reason for plagiarism. Studies have shown that students in STEM are more apt to plagiarize as compared to students in the humanities or social sciences, so they are an ideal population for looking at causes of plagiarism. The goal of this study was to examine college STEM student self-reported frequencies of plagiarism, ability to recognize instances of plagiarism, and justifications for why certain acts of plagiarism may or may not be acceptable. Surveys were collected from 965 STEM students taking an introductory biology class. The majority of freshmen surveyed admitted to some degree of plagiarism and found it difficult to recognize certain types of plagiarism. Juniors and seniors were less likely to report any form of plagiarism and are better able to recognize specific types, supporting previous work that point at lack of experience as the reason for most plagiarism in college. However, students at all levels were confused about the acceptability of some examples of plagiarism, such as reusing the same paper in multiple classes and some students point to external factors like grading practices in previous courses as motivators for certain types of plagiarism. Fully understanding where students still struggle to recognize plagiarism and their motivations for committing certain types of plagiarism will help in creating strategies to mitigate this common problem.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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: no | Qualitative | low |
| gpt | Research integrity Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | 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 | 0.007 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".