Checklists to Detect Potential Predatory Biomedical Journals: A Systematic Review
Bibliographic record
Abstract
ABSTRACT Background We believe there is a large number of checklists to help authors detect predatory journals. It is uncertain whether these checklists contain similar content. Purpose Perform a systematic review to identify checklists to detect potential predatory journals and to examine their content and measurement properties. Data Sources MEDLINE, Embase, PsycINFO, ERIC, Web of Science and Library, Information Science & Technology Abstracts (January 2012 to November 2018), university library websites (January 2019), YouTube (January 2019). Study Selection Original checklists used to detect potential predatory journals published in English, French or Portuguese, with instructions in point form, bullet form, tabular format or listed items, not including lists or guidance on recognizing “legitimate” or “trustworthy” journals. Data Extraction Pairs of reviewers independently extracted study data and assessed checklist quality and a third reviewer resolved conflicts. Data Synthesis Of 1528 records screened, 93 met our inclusion criteria. The majority of included checklists were in English (n = 90, 97%), could be completed in fewer than five minutes (n = 68, 73%), had an average of 11 items, which were not weighted (n = 91, 98%), did not include qualitative guidance (n = 78, 84%) or quantitative guidance (n = 91, 98%), were not evidence-based (n = 90, 97%) and covered a mean of four (of six) thematic categories. Only three met our criteria for being evidence-based. Limitations Limited languages and years of publication, searching other media. Conclusions There is a plethora of published checklists that may overwhelm authors looking to efficiently guard against publishing in predatory journals. The similarity in checklists could lead to the creation of evidence-based tools serving authors from all disciplines. Funding Source This project received no specific funding. David Moher is supported by a University Research Chair (University of Ottawa). Danielle Rice is supported by a Canadian Institutes of Health Research Health Systems Impact Fellowship; Lucas Helal is supported by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001, PDSE - 88881.189100/2018 - 01. Manoj Lalu is supported by The Ottawa Hospital Anesthesia Alternate Funds Association.
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 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 | 0.093 | 0.197 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.086 | 0.239 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.011 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.021 |
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; both teacher heads 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".