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Record W2889499003 · doi:10.1503/cmaj.180154

How predatory journals leak into PubMed

2018· article· en· W2889499003 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.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCanadian Medical Association Journal · 2018
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsOttawa Hospital
FundersUniversity of Ottawa
KeywordsKey (lock)Data scienceComputer scienceMEDLINEWorld Wide WebComputer securityPolitical science

Abstract

fetched live from OpenAlex

KEY POINTS Recent reports that PubMed, one of the world’s leading biomedical databases, includes predatory journals and their publications[1][1],[2][2] is cause for concern. PubMed handles millions of queries daily and represents a key source of knowledge for health researchers worldwide. Much

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.

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
gemmaMetaresearchBibliometricsResearch integrity
Domain: Evaluation · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptMetaresearchBibliometricsScholarly communicationResearch integrity
Domain: Evaluation · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalmedium
models splitAgreement compares identical category sets and study designs across arms.

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)0.081
metaresearch head score (Gemma)0.306
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Bibliometrics, 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 score0.556
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0810.306
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0340.065
Science and technology studies0.0010.000
Scholarly communication0.0110.001
Open science0.0030.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.333
GPT teacher head0.496
Teacher spread0.163 · 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