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Comparing companion open access journals to their traditional journal counterparts

2025· article· W7092365531 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2025
Typearticle
Language
FieldDecision Sciences
TopicAcademic Publishing and Open Access
Canadian institutionsnot available
Fundersnot available
KeywordsPublishingCitationPublicationDemographicsBibliometricsScope (computer science)Quarter (Canadian coin)

Abstract

fetched live from OpenAlex

Many traditional journals have launched companion open access (cOA) journals with similar scope and aims. These journals seek better article dissemination through removal of the paywall and use of article processing charges (APCs). Traditional journals often suggest transfer to their cOA journal, leaving authors with a decision to accept transfer and pay an APC or resubmit elsewhere. We aim to compare costs and impact of these journals to better inform authors. The top 15 U.S.-based traditional journals within medicine, surgery, pediatrics, and OB/GYN were identified based on 2023 impact factor. Those with cOA journals were included, and all publication data between 2011 and 2023 were extracted. Citation counts were compared using Poisson regression; author demographics were analyzed using multivariable logistic regression. There were 14 traditional journals with cOA counterparts, constituting 52,232 publications from 36,577 authors. cOA articles had half the citations of traditional publications (9.4 vs 18.2) and collected an estimated $35 million in APCs. Female and low/middle income country (LMIC) authors were more likely to publish in cOA journals (aOR = 1.23, 1.14, respectively). Authors publishing in companion open access journals incur higher publication costs, and yet, receive fewer citations per publication.

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)0.006
metaresearch head score (Gemma)0.021
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesScholarly communication, Open science, 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 score0.947
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.021
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0030.000
Scholarly communication0.1370.026
Open science0.0360.013
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.4980.005

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 head0.579
GPT teacher head0.523
Teacher spread0.056 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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