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Record W4389309117 · doi:10.1002/jrsm.1684

Predatory journals and their practices present a conundrum for systematic reviewers and evidence synthesisers of health research: A qualitative descriptive study

2023· article· en· W4389309117 on OpenAlexafffund
Danielle Pollock, Timothy Hugh Barker, Jennifer Stone, Edoardo Aromataris, Miloslav Klugar, Anna Mae Scott, Cindy Stern, Amanda Ross‐White, Ashley Whitehorn, Rick Wiechula, Larissa Shamseer, Zachary Munn

Bibliographic record

VenueResearch Synthesis Methods · 2023
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsSt. Michael's HospitalQueen's University
FundersNational Health and Medical Research CouncilMedical Research CouncilCanadian Institutes of Health Research
KeywordsPublishingInclusion (mineral)Evidence-based practiceBest evidenceCritical appraisalQualitative researchPsychologyPublic relationsPolitical scienceSocial scienceMedical educationSociologyAlternative medicineSocial psychologyMedicineLaw

Abstract

fetched live from OpenAlex

Predatory journals are a blemish on scholarly publishing and academia and the studies published within them are more likely to contain data that is false. The inclusion of studies from predatory journals in evidence syntheses is potentially problematic due to this propensity for false data to be included. To date, there has been little exploration of the opinions and experiences of evidence synthesisers when dealing with predatory journals in the conduct of their evidence synthesis. In this paper, the thoughts, opinions, and attitudes of evidence synthesisers towards predatory journals and the inclusion of studies published within these journals in evidence syntheses were sought. Focus groups were held with participants who were experienced evidence synthesisers from JBI (previously the Joanna Briggs Institute) collaboration. Utilising qualitative content analysis, two generic categories were identified: predatory journals within evidence synthesis, and predatory journals within academia. Our findings suggest that evidence synthesisers believe predatory journals are hard to identify and that there is no current consensus on the management of these studies if they have been included in an evidence synthesis. There is a critical need for further research, education, guidance, and development of clear processes to assist evidence synthesisers in the management of studies from predatory journals.

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

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 armCategoriesStudy designConfidence
gemmaMetaresearchResearch integrity
Domain: Evaluation · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativelow
gptMetaresearchScholarly communicationResearch integrity
Domain: Evaluation · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativelow
models splitAgreement compares identical category sets and study designs across arms.

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.242
metaresearch head score (Gemma)0.482
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.995
Threshold uncertainty score0.935

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2420.482
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0090.012
Science and technology studies0.0130.024
Scholarly communication0.0200.018
Open science0.0030.014
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.987
GPT teacher head0.822
Teacher spread0.164 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Labeled directly by 2 models reading the full record.

MetaresearchResearch integrityScholarly communication

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designQualitative
DomainEvaluation
GenreEmpirical

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".

Quick stats

Citations10
Published2023
Admission routes2
Has abstractyes

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