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Record W6969617023 · doi:10.5281/zenodo.6557270

Inequities of Article Processing Charges: How the Oligopoly of Academic Publishers Profits from Open Access

2022· article· en· W6969617023 on OpenAlexaffabout

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2022
Typearticle
Languageen
FieldDecision Sciences
TopicAcademic Publishing and Open Access
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsOligopolyPublishingEquity (law)Presentation (obstetrics)RevenueRevenue model

Abstract

fetched live from OpenAlex

<em>Public lecture at the Knowledge Equity and Justice Spring Seminar, 17 May 2022, organized by Stacy Allison-Cassin and SPARC</em> Since the early 2010s, more than half of peer-reviewed journal articles have been published by the so-called oligopoly of academic publishers: Elsevier, SAGE, Springer-Nature, Taylor &amp; Francis and Wiley. These companies make immense profits from publishing scholarly journals, traditionally through subscriptions from academic libraries, the reader pays model. With more and more libraries cancelling so-called ‘Big Deals’, these publishers have expanded their revenues by making authors pay article processing charges (APCs) for open access (OA) publishing. The author-pays model creates inequities and barriers that exclude many from publishing, such as underrepresented groups or researchers from less-resourced countries. This presentation demonstrates the growth of gold and hybrid OA articles published in oligopoly journals indexed in the Web of Science and provides evidence of the amount of APCs paid in Canada and globally. It highlights the inequities of the author-pays model and discusses alternative routes to OA.

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)0.008
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication, Open science, Insufficient payload (model declined to judge)
Consensus categoriesOpen science
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.256
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0030.000
Scholarly communication0.0110.008
Open science0.0200.019
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.000

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 head0.239
GPT teacher head0.404
Teacher spread0.165 · 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
Published2022
Admission routes2
Has abstractyes

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