Should I include studies from “predatory” journals in a systematic review? Interim guidance for systematic reviewers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.285 | 0.841 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.020 | 0.005 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it