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Record W4387860045 · doi:10.1002/pra2.809

“I Am in a Privileged Situation”: Examining the Factors Promoting Inequity in Open Access Publishing

2023· article· en· W4387860045 on OpenAlexaffabout
Philips Ayeni

Bibliographic record

VenueProceedings of the Association for Information Science and Technology · 2023
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsMcGill University
Fundersnot available
KeywordsPublishingEquity (law)Public relationsThematic analysisScholarly communicationReflexivityPolitical scienceDisciplineOpen access publishingQualitative researchSociologySocial scienceLibrary scienceLaw

Abstract

fetched live from OpenAlex

ABSTRACT Despite increasing advocacy for open access (OA), the uptake of OA in some disciplines has remained low. Existing studies have linked the low uptake in OA publishing in the humanities and social sciences (HSS) to disciplinary norm, limited funding to pay for article processing charges (APCs), and researchers' preferences. However, there is a growing concern about inequity in OA scholarly communication, as it has remained inaccessible and unaffordable to many researchers. This study therefore investigated inequity in OA publishing in Canada. Using semi‐structured interviews, qualitative data was collected from 20 professors from the HSS disciplines of research‐intensive universities in Canada. Data was analyzed with NVivo software following the reflexive thematic analysis approach. Findings revealed three main causes of inequity in OA publishing among the participants. These are the cost of APCs, unequal privileges, and gender disparities. Hence, there is a need for concerted efforts by funding agencies, stakeholders, higher education institutions, and researchers to promote equity in OA scholarly communication. Some recommendations for improving equity in OA publishing are provided in this paper.

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)0.070
metaresearch head score (Gemma)0.318
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics, Scholarly communication, Open science
Consensus categoriesMetaresearch, Bibliometrics, Scholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0700.318
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0320.241
Science and technology studies0.0010.000
Scholarly communication0.0110.025
Open science0.0070.005
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 head0.476
GPT teacher head0.535
Teacher spread0.059 · 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

Citations2
Published2023
Admission routes2
Has abstractyes

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