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Record W4388465513 · doi:10.1162/qss_a_00272

The oligopoly’s shift to open access: How the big five academic publishers profit from article processing charges

2023· article· en· W4388465513 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueQuantitative Science Studies · 2023
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsUniversité de MontréalUniversité du Québec à MontréalDalhousie UniversityUniversity of Ottawa
Fundersnot available
KeywordsRevenuePublicationPublishingOligopolyLibrary scienceThe InternetEconomicsBusinessPolitical scienceAdvertisingComputer scienceMathematical economicsFinanceWorld Wide WebLaw

Abstract

fetched live from OpenAlex

Abstract We aim to estimate the total amount of article processing charges (APCs) paid to publish open access (OA) in journals controlled by the five large commercial publishers (Elsevier, Sage, Springer Nature, Taylor & Francis, and Wiley) between 2015 and 2018. Using publication data from WoS, OA status from Unpaywall, and annual APC prices from open data sets and historical fees retrieved via the Internet Archive Wayback Machine, we estimate that globally authors paid $1.06 billion in publication fees to these publishers from 2015–2018. Revenue from gold OA amounted to $612.5 million, and $448.3 million was obtained for publishing OA in hybrid journals. Among the five publishers, Springer Nature made the most revenue from OA ($589.7 million), followed by Elsevier ($221.4 million), Wiley ($114.3 million), Taylor & Francis ($76.8 million), and Sage ($31.6 million). With Elsevier and Wiley making most of their APC revenue from hybrid fees and others focusing on gold, different OA strategies could be observed between publishers.

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.

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)0.051
metaresearch head score (Gemma)0.202
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics, Science and technology studies, Scholarly communication, Open science
Consensus categoriesMetaresearch, Bibliometrics, Science and technology studies, Open science
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.515
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0510.202
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0130.302
Science and technology studies0.0060.004
Scholarly communication0.0490.010
Open science0.0210.014
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.878
GPT teacher head0.694
Teacher spread0.184 · 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