Predatory publications in evidence syntheses
Bibliographic record
Abstract
OBJECTIVES: The number of predatory journals is increasing in the scholarly communication realm. These journals use questionable business practices, minimal or no peer review, or limited editorial oversight and, thus, publish articles below a minimally accepted standard of quality. These publications have the potential to alter the results of knowledge syntheses. The objective of this study was to determine the degree to which articles published by a major predatory publisher in the health and biomedical sciences are cited in systematic reviews. METHODS: The authors downloaded citations of articles published by a known predatory publisher. Using forward reference searching in Google Scholar, we examined whether these publications were cited in systematic reviews. RESULTS: The selected predatory publisher published 459 journals in the health and biomedical sciences. Sixty-two of these journal titles had published a total of 120 articles that were cited by at least 1 systematic review, with a total of 157 systematic reviews citing an article from 1 of these predatory journals. DISCUSSION: Systematic review authors should be vigilant for predatory journals that can appear to be legitimate. To reduce the risk of including articles from predatory journals in knowledge syntheses, systematic reviewers should use a checklist to ensure a measure of quality control for included papers and be aware that Google Scholar and PubMed do not provide the same level of quality control as other bibliographic databases.
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
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 | 0.466 | 0.833 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.035 | 0.048 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.024 | 0.022 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".