Stress testing journals: a quasi-experimental study of rejection rates of a previously published paper
Bibliographic record
Abstract
Abstract Background When a journal receives a duplicate publication, the ability to identify the submitted work as previously published, and reject it, is an assay to publication ethics best practices. The aim of this study was to evaluate how three different types of journals, namely open access (OA) journals, subscription-based journals, and presumed predatory journals, responded to receiving a previously published manuscript for review. Methods We performed a quasi-experimental study in which we submitted a previously published article to a random sample of 602 biomedical journals, roughly 200 journals from each journal type sampled: OA journals, subscription-based journals, and presumed predatory journals. Three hundred and three journals received a Word version in manuscript format, while 299 journals received the formatted publisher’s PDF version of the published article. We then recorded responses to the submission received after approximately 1 month. Responses were reviewed, extracted, and coded in duplicate. Our primary outcome was the rate of rejection of the two types of submitted articles (PDF vs Word) within our three journal types. Results We received correspondence back from 308 (51.1%) journals within our study timeline (32 days); (N = 46 predatory journals, N = 127 OA journals, N = 135 subscription-based journals). Of the journals that responded, 153 received the Word version of the paper, while 155 received the PDF version. Four journals (1.3%) accepted our paper, 291 (94.5%) journals rejected the paper, and 13 (4.2%) requested a revision. A chi-square test looking at journal type, and submission type, was significant (χ2 (4) = 23.50, p < 0.001). All four responses to accept our article came from presumed predatory journals, 3 of which received the Word format and 1 that received the PDF format. Less than half of journals that rejected our submissions did so because they identified ethical issues such as plagiarism with the manuscript (133 (45.7%)). Conclusion Few journals accepted our submitted paper. However, our findings suggest that all three types of journals may not have adequate safeguards in place to recognize and act on plagiarism or duplicate submissions.
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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 | 0.058 | 0.176 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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, 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".