Self-plagiarism: a misnomer
Bibliographic record
Abstract
The opprobrium of self-plagiarism makes one a scientific pariah. This paper provides a critical evaluation of the discourse of “self-plagiarism” in scientific publications. We first show that “self-plagiarism” is a misnomer and should be replaced with “unacceptable duplication.” We distinguish acceptable from unacceptable duplication in scientific communications, providing a typology of both. We then review issues of copyright infringement and propose a preventive ethics approach to unacceptable duplication. Scientific misconduct and self-plagiarismAmerican Journal of Obstetrics & GynecologyVol. 214Issue 4PreviewThe recent report on self-plagiarism is very interesting.1 Mandal et al2 recently reported an Indian perspective, mentioned several problems referring to scientific misconduct, and noted that self-plagiarism was a very common problem. In fact, the problem can be seen worldwide. Here, we would like to share our experience. First, in the context of non-English speaking countries, such as Thailand, scientific misconduct might be observable in the form of “translational plagiarism.”3 This can be seen in several publications, including reports by eminent researchers from local universities. Full-Text PDF ReplyAmerican Journal of Obstetrics & GynecologyVol. 214Issue 4PreviewWe appreciate the comments of Drs Joob and Wiwanitkit with regard to our recent article.1 Most of their comments (eg, “translational plagiarism”) are on plagiarism and not self- plagiarism, but some are relevant to both. The authors of this letter are correct that the response to plagiarism or unacceptable duplication is quite variable and at times minimal. The medical and scientific community may need to address this issue and possibly create a set of rules for detection and response to these issues. Full-Text PDF
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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 | 0.069 | 0.223 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.012 | 0.068 |
| Scholarly communication | 0.014 | 0.026 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.012 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".