Should I include studies from “predatory” journals in a systematic review? Interim guidance for systematic reviewers
Bibliographic record
Abstract
ABSTRACT: A systematic review involves the identification, evaluation, and synthesis of the best-available evidence to provide an answer to a specific question. The "best-available evidence" is, in many cases, a peer-reviewed scientific article published in an academic journal that details the conduct and results of a scientific study. Any potential threat to the validity of these individual studies (and hence the resultant synthesis) must be evaluated and critiqued.In science, the number of predatory journals continue to rise. Studies published in predatory journals may be of lower quality and more likely to be impacted by fraud and error compared to studies published in traditional journals. This poses a threat to the validity of systematic reviews that include these studies and, therefore, the translation of evidence into guidance for policy and practice. Despite the challenges predatory journals present to systematic reviewers, there is currently little guidance regarding how they should be managed.In 2020, a subgroup of the JBI Scientific Committee was formed to investigate this issue. In this overview paper, we introduce predatory journals to systematic reviewers, outline the problems they present and their potential impact on systematic reviews, and provide some alternative strategies for consideration of studies from predatory journals in systematic reviews. Options for systematic reviewers could include excluding all studies from suspected predatory journals, applying additional strategies to forensically examine the results of studies published in suspected predatory journals, setting stringent search limits, and applying analytical techniques (such as subgroup or sensitivity analyses) to investigate the impact of suspected predatory journals in a synthesis.
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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 | 0.633 | 0.890 |
| Meta-epidemiology (narrow) | 0.006 | 0.011 |
| Meta-epidemiology (broad) | 0.017 | 0.017 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.024 | 0.033 |
| Open science | 0.014 | 0.009 |
| Research integrity | 0.051 | 0.029 |
| Insufficient payload (model declined to judge) | 0.014 | 0.017 |
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".