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Record W3081624309

PREDATORY JOURNALS: A GLOBAL THREAT TO THE SCHOLARLY PUBLISHING LANDSCAPE

2020· article· en· W3081624309 on OpenAlexaboutno aff
Faaiz Ali Shah, Zahid Nazar

Bibliographic record

VenueJournal of Postgraduate Medical Institute · 2020
Typearticle
Languageen
FieldDecision Sciences
TopicAcademic Publishing and Open Access
Canadian institutionsnot available
Fundersnot available
KeywordsPublicationPublishingScholarshipTransparency (behavior)Library scienceInternet privacyPolitical scienceLawComputer science
DOInot available

Abstract

fetched live from OpenAlex

There has been a major trend in academic publishing from traditional print publication to open access journals and online publication in recent times. The authors now find more avenues to publish their work than before, but at the same time vulnerable to become prey to predatory journals. The term ”Predatory Journals” was first coined by Jeffrey Beall 1 , Associate Professor and librarian at the university of Colorado Denver. According to Beall predatory publisher is one“which publish counterfeit journals to exploit the open access model in which the author pays.” The most recent clear, comprehensive and consensus definition of predatory journal was formulated in April 2019 in Ottawa, Canada by 43 researchers belonging to 10 different countries. 2 Their definition reads“ Predatory journals and publishers are entities that prioritize self interest at the expense of scholarship and are characterized by false or misleading information, deviation from best editorial and publication practices, a lack of transparency, and/or the use of aggressive and indiscriminate solicitation practices.”Alternative terms for predatory journals are “Dark Journals,” 2 “Illegitimate Journals” 3,4 “Deceptive Journals” 5 Pseudo Journals” 6 and “Journals Operating in Bad Faith” 7

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
gemmaMetaresearchScholarly communication
Domain: Evaluation · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptuallow
gptScholarly communicationResearch integrity
Domain: not available · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Not applicablemedium
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)0.023
metaresearch head score (Gemma)0.207
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication, Open science, Research integrity
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.719
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0230.207
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0190.030
Open science0.0100.001
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0010.000

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 head0.213
GPT teacher head0.431
Teacher spread0.218 · 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.

MetaresearchScholarly communicationResearch integrity

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

Study designTheoretical or conceptual · Not applicable
DomainEvaluation
GenreEmpirical · Commentary

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

Citations1
Published2020
Admission routes1
Has abstractyes

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