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Record W3110679542 · doi:10.1136/bmj.m4265

Predatory journals enter biomedical databases through public funding

2020· article· en· W3110679542 on OpenAlexaff
Andrea Manca, Lucia Cugusi, Andrea Cortegiani, Giulia Ingoglia, David Moher, Franca Deriu

Bibliographic record

VenueBMJ · 2020
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsWorld Wide WebData scienceComputer scienceInternet privacyMedicineDatabase

Abstract

fetched live from OpenAlex

Predatory publishing is an international, cross disciplinary threat to the integrity of the scientific system • A worrying number of articles published in predatory journals are indexed in biomedical databases such as PubMed, the free access biomedical database maintained by the National Library of Medicine • Public funding, under open access policies, seems to be the mechanism by which articles are displayed in PubMed. • Providing guidance to publicly funded authors on how to publish their work in legitimate open-access venues will likely stop the wasteful use of public money to cover the fees of 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

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)0.018
metaresearch head score (Gemma)0.116
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesBibliometrics, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.651
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.116
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0160.124
Science and technology studies0.0000.000
Scholarly communication0.0030.002
Open science0.0030.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.002

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.

Opus teacher head0.909
GPT teacher head0.660
Teacher spread0.249 · 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

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
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

Citations49
Published2020
Admission routes1
Has abstractyes

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