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Record W2587252982 · doi:10.18192/uojm.v7i1.1755

Predatory Journals : Do Not Enter

2017· article· en· W2587252982 on OpenAlexaffvenue
Faizan Khan, David Moher

Bibliographic record

VenueUniversity of Ottawa Journal of Medicine · 2017
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsOttawa Public HealthOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsHumanitiesAudience measurementPolitical scienceCredibilityPublishingLibrary scienceArtLawComputer science

Abstract

fetched live from OpenAlex

AbstractThe escalation in open-access publishing has fueled the rise of questionable businesses, namely, ‘predatory’ journals. Predatory journals and their publishers seek manuscripts through aggressive electronic solicitation and execute flawed peer-review practices, consequently undermining the scholarly record and current research cultures, globally. As these journals are not indexed in any legitimate databases, the research they publish is often undiscoverable and fails to be disseminated to a worldwide readership. Acknowledging this threat to the credibility of science, this article aims to alert researchers at various levels of their career of the increasing global issue of predatory journals, and offer helpful advice and resources for identifying and avoiding them. RésuméL’essor de la publication en accès libre a alimenté l’expansion d’entreprises douteuses, à savoir des revues « prédatrices ». Les revues prédatrices et leurs éditeurs recherchent des manuscrits au moyen de requêtes électroniques agressives et effectuent des évaluations erronées par les pairs, compromettant ainsi les archives scientifiques et les présentes cultures de recherche dans l’ensemble. Puisque ces revues ne sont pas répertoriées dans des bases de données légitimes, la recherche qu’elles publient est souvent introuvable et ne parvient pas à être diffusée à un lectorat mondial. En reconnaissant cette menace à la crédibilité scientifique, cet article vise à alerter les chercheurs à diverses étapes de leur carrière du problème global croissant que constituent les revues prédatrices, et à leur offrir des conseils utiles et des ressources permettant de les identifier et de les éviter.

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
Not applicablelow
gptMetaresearchScholarly communicationResearch integrity
Domain: Evaluation · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Not applicablehigh
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)0.027
metaresearch head score (Gemma)0.034
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.173
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0270.034
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0230.007
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0050.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.518
GPT teacher head0.536
Teacher spread0.018 · 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 designNot 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

Citations9
Published2017
Admission routes2
Has abstractyes

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