Do Journals’ Author Guidelines Tell Us What We Need to Know about Plagiarism?
Bibliographic record
Abstract
Author guidelines for submitting manuscripts to journals play an essential role in communicating academic ethics and standards to prospective authors and in ensuring the originality of the articles that journals publish. The purpose of this study was to conduct a cross-journal analysis of author guidelines to see how they address plagiarism. One hundred author guidelines were selected randomly and were analyzed qualitatively and quantitatively. The findings revealed that the guidelines varied in the extent to which they covered plagiarism. Among the elements of plagiarism addressed, the four most common were duplicate publication, copyright, the definition of plagiarism of others’ work, and proper citation. The allowance for reproduction of language ranged along a spectrum from very strict (no verbatim copying of another’s words) to less strict (no verbatim copying of significant portions of others’ work). Although self-plagiarism is the most common form of plagiarism, it was addressed relatively less often than plagiarism of others’ work.
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 | MetaresearchResearch integrity Domain: Reporting · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | Scholarly communicationResearch integrity Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | 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 | 0.031 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.097 | 0.125 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.014 |
| Insufficient payload (model declined to judge) | 0.001 | 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, 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".