Academic Misconduct and Plagiarism
Bibliographic record
Abstract
<JATS1:p>This book discusses the issue of academic misconduct and publication ethics in general and plagiarism in particular, with a focus on case studies in various universities around the world (notably in Japan, Singapore, Australia, USA, and Canada). We are especially interested in students’ and teachers’ perception of academic misconduct and their definition and understanding of plagiarism. Most chapters discuss undergraduates’ understanding of academic dishonesty and students’ experiences using plagiarism softwares. The book also analyzes teachers’ perception of cheating and how they respond to it. Writing is perceived by all of the teachers to be the most important form of assessment that required preventative measures in order to reduce the occurrence of academic dishonesty among students. Each chapter recommends strategies to fight plagiarism, such as establishing guidelines and regulations concerning academic integrity, awareness of the scale of the issue (scandals at all levels in most countries, even including famous scholars, administrators, and elected officials), assessing the damage done to academic reputation and credibility, developing trust and credibility on social media (especially with the recent disturbing growth of fake news and data), minimizing the proliferation of dishonest accreditation, of identity theft, of fake peer-reviews, and fighting the growing number of fake papers, with or without the use of computer-generated academic works.</JATS1:p>
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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: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | Research integrity Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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.
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".