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Record W2560530650 · doi:10.1002/ase.1671

Academic nightmares: Predatory publishing

2016· article· en· W2560530650 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.

Bibliographic record

VenueAnatomical Sciences Education · 2016
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsWestern University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPublishingPublicationTransparency (behavior)DiligencePublic relationsScientific misconductWork (physics)Political sciencePsychologyMedicineEngineeringLawAlternative medicine

Abstract

fetched live from OpenAlex

Academic researchers who seek to publish their work are confronted daily with a barrage of e-mails from aggressive marketing campaigns that solicit them to publish their research with a specialized, often newly launched, journal. Known as predatory journals, they often promise high editorial and publishing standards, yet their exploitive business models, poor quality control, and minimal overall transparency victimize those researchers with limited academic experience and pave the way for low-quality articles that threaten the foundation of evidence-based research. Understanding how to identify these predatory journals requires thorough due diligence on the part of the submitting authors, and a commitment by reputable publishers, institutions, and researchers to publicly identify these predators and eliminate them as a threat to the careers of young scientists seeking to disseminate their work in scholarly journals. Anat Sci Educ 10: 392-394. © 2016 American Association of Anatomists.

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)0.024
metaresearch head score (Gemma)0.080
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.673
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0240.080
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0200.085
Science and technology studies0.0000.001
Scholarly communication0.0040.006
Open science0.0040.001
Research integrity0.0000.000
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 head0.507
GPT teacher head0.589
Teacher spread0.082 · 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