Um ano de e-mails não solicitados: o modus operandi de revistas e editoras predatórias
Bibliographic record
Abstract
Objectives: To quantify, characterize and analyze e-mail from predatory journals (PJ) received by an academic in dentistry. Materials and methods: E-mails received in 2019 and suspected of being potentially predatory were pre-selected. The Ottawa Hospital Research Institute (OHRI) checklist was applied to identify the suspected biomedical PJ, including the following criteria: article processing charge (APC), fake impact factor, the journal being listed in the Directory of Open Access Journals (DOAJ) and the Committee on Publication Ethics (COPE). We also extracted information on the lack of an impact factor on Journal Citations Reports, non-journal affiliated contact e-mail address, flattering language, article and/or personal citation, unsubscribe link, being listed in the National Library of Medicine (NLM) current catalog and indexed on Medline. Results: A total of 2,812 unsolicited suspected e-mails were received, and 1,837 requested some sort of manuscript; among these, 1,751 met some of the OHRI criteria. Less than half (780/1,837, 42%) referred to some area of dentistry. The median APC was US$399. A false impact factor was mentioned in 11% (201/1,837) of the e-mails, and 27% (504/1,837) corresponded to journals currently listed in the NLM catalog. Journals listed in DOAJ and COPE sent 89 e-mails. Conclusions: The email campaign from PJ was high and recurrent. Researchers should be well informed about PJ’ modus operandi to protect their own reputation as authors and that of science.
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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 | 0.032 | 0.279 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".