How should we handle predatory journals in evidence synthesis? A descriptive survey‐based cross‐sectional study of evidence synthesis experts
Bibliographic record
Abstract
Synthesizers of evidence are increasingly likely to encounter studies published in predatory journals during the evidence synthesis process. The evidence synthesis discipline is uniquely positioned to encounter novel concerns associated with predatory journals. The objective of this research was to explore the attitudes, opinions, and experiences of experts in the synthesis of evidence regarding predatory journals. Employing a descriptive survey-based cross-sectional study design, these experts were asked a series of questions regarding predatory journals to explore these attitudes, opinions, and experiences. Two hundred and sixty four evidence synthesis experts responded to this survey. Most respondents agreed with the definition of a predatory journal (86%), however several (19%) responded that this definition was difficult to apply practically. Many respondents believed that studies published in predatory journals are still eligible for inclusion into an evidence synthesis project. However, this was only after the study had been determined to be 'high-quality' (39%) or if the results were validated (13%). While many respondents could identify common characteristics of these journals, there was still hesitancy regarding the appropriate methods to follow when considering including these studies into an evidence synthesis project.
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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 | 0.457 | 0.674 |
| Meta-epidemiology (narrow) | 0.000 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".