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Record W4388513251 · doi:10.1002/asi.24844

Varieties of diffusion in academic publishing: How status and legitimacy influence growth trajectories of new innovations

2023· article· en· W4388513251 on OpenAlexaff
Kyle Siler, Vincent Larivière

Bibliographic record

VenueJournal of the Association for Information Science and Technology · 2023
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsUniversité de MontréalUniversity of Toronto
FundersAlfred P. Sloan Foundation
KeywordsPublishingContext (archaeology)LegitimacyDiffusionSocial sciencePolitical scienceSociologyEconomic geographyDemographyDemographic economicsHistoryGeographyLawPoliticsEconomics

Abstract

fetched live from OpenAlex

Abstract Open Access (OA) publishing has progressed from an initial fringe idea to a still‐growing, major component of modern academic communication. The proliferation of OA publishing presents a context to examine how new innovations and institutions develop. Based on analyses of 1,296,304 articles published in 83 OA journals, we analyze changes in the institutional status, gender, age, citedness, and geographical locations of authors over time. Generally, OA journals tended towards core‐to‐periphery diffusion patterns. Specifically, journal authors tended to decrease in high‐status institutional affiliations, male and highly cited authors over time. Despite these general tendencies, there was substantial variation in the diffusion patterns of OA journals. Some journals exhibited no significant demographic changes, and a few exhibited periphery‐to‐core diffusion patterns. We find that although both highly and less‐legitimate journals generally exhibit core‐to‐periphery diffusion patterns, there are still demographic differences between such journals. Institutional and cultural legitimacy—or lack thereof—affects the social and intellectual diffusion of new OA journals.

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)0.021
metaresearch head score (Gemma)0.234
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics
Consensus categoriesBibliometrics
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.213
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0210.234
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0380.140
Science and technology studies0.0000.000
Scholarly communication0.0010.009
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.217
GPT teacher head0.468
Teacher spread0.251 · 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 designObservational
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

Citations6
Published2023
Admission routes1
Has abstractyes

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