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Record W4313648545 · doi:10.1002/jrsm.1613

How should we handle predatory journals in evidence synthesis? A descriptive survey‐based cross‐sectional study of evidence synthesis experts

2023· article· en· W4313648545 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueResearch Synthesis Methods · 2023
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsQueen's University
FundersHealth Research
KeywordsInclusion (mineral)PsychologyQuality (philosophy)Medical educationSurvey researchMedicineApplied psychologySocial psychology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.754
metaresearch head score (Gemma)0.909
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Scholarly communication, Open science, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.268
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.7540.909
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0060.002
Bibliometrics0.0060.014
Science and technology studies0.0010.001
Scholarly communication0.0050.002
Open science0.0070.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.981
GPT teacher head0.728
Teacher spread0.253 · 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