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Record W4391607557 · doi:10.1002/ijgo.15398

Open access transition in obstetrics and gynecology journals—The international impact

2024· article· en· W4391607557 on OpenAlexaff
Gabriel Levin, Yoav Brezinov, Yossi Tzur, Raanan Meyer

Bibliographic record

VenueInternational Journal of Gynecology & Obstetrics · 2024
Typearticle
Languageen
FieldDecision Sciences
TopicAcademic Publishing and Open Access
Canadian institutionsMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsMedicinePublishingObstetrics and gynaecologyCitationDemographyLibrary scienceGynecologyObstetricsPregnancyPolitical scienceSociology

Abstract

fetched live from OpenAlex

OBJECTIVE: To study the impact of converting from subscription-based publishing to open access ("flipping") in three obstetrics and gynecology (OBGYN) journals. METHODS: We compared original articles in three OBGYN journals during a matched subscription-based and open access publishing period. We analyzed citation metrics and country of authorship. RESULTS: Overall, 1522 studies were included; of those, 869 (57.1%) were before flipping and 653 (42.9%) were after flipping. There was a decrease in publications by lower-middle income countries from 7.7% in subscription-based publishing to 1.8% in open access (P < 0.001). There was a decrease in the proportion of articles from South Asia (2.5% vs 0.5%), North America (14.4% vs 9.4%), and the Middle East (7.4% vs 2.5%), and an increase in publications from East Asia and Pacific (17.4% vs 30.9%; P < 0.001). The relative citation ratio was higher in the open access period (median 1.65 vs 0.95, P < 0.001). The number of citations per year was higher in the open access period (median 3.0 vs 2.0, P < 0.001). There was an increase in the proportion of funded studies (from 40.2% to 47.8%; P = 0.003). CONCLUSIONS: Flipping to open access in OBGYN journals is associated with a citation advantage with major authorship changes, leading to inequity.

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)0.011
metaresearch head score (Gemma)0.186
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication, Open science, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.795
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.186
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.003
Science and technology studies0.0000.000
Scholarly communication0.0120.009
Open science0.0150.002
Research integrity0.0000.002
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 head0.132
GPT teacher head0.490
Teacher spread0.359 · 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; a candidate call from one teacher head, not a consensus.

Study designOther design
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

Citations3
Published2024
Admission routes1
Has abstractyes

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