Defining predatory journals and responding to the threat they pose: a modified Delphi consensus process
Bibliographic record
Abstract
OBJECTIVE: To conduct a Delphi survey informing a consensus definition of predatory journals and publishers. DESIGN: This is a modified three-round Delphi survey delivered online for the first two rounds and in-person for the third round. Questions encompassed three themes: (1) predatory journal definition; (2) educational outreach and policy initiatives on predatory publishing; and (3) developing technological solutions to stop submissions to predatory journals and other low-quality journals. PARTICIPANTS: Through snowball and purposive sampling of targeted experts, we identified 45 noted experts in predatory journals and journalology. The international group included funders, academics and representatives of academic institutions, librarians and information scientists, policy makers, journal editors, publishers, researchers involved in studying predatory journals and legitimate journals, and patient partners. In addition, 198 authors of articles discussing predatory journals were invited to participate in round 1. RESULTS: A total of 115 individuals (107 in round 1 and 45 in rounds 2 and 3) completed the survey on predatory journals and publishers. We reached consensus on 18 items out of a total of 33 to be included in a consensus definition of predatory journals and publishers. We came to consensus on educational outreach and policy initiatives on which to focus, including the development of a single checklist to detect predatory journals and publishers, and public funding to support research in this general area. We identified technological solutions to address the problem: a 'one-stop-shop' website to consolidate information on the topic and a 'predatory journal research observatory' to identify ongoing research and analysis about predatory journals/publishers. CONCLUSIONS: In bringing together an international group of diverse stakeholders, we were able to use a modified Delphi process to inform the development of a definition of predatory journals and publishers. This definition will help institutions, funders and other stakeholders generate practical guidance on avoiding predatory journals and publishers.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchScholarly communication Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | MetaresearchResearch integrityScholarly communication Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | medium |
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 | 0.271 | 0.174 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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 itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".