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Record W7105698005 · doi:10.5281/zenodo.17594289

The Drain of Scientific Publishing Infographic

2025· article· en· W7105698005 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2025
Typearticle
Languageen
FieldDecision Sciences
TopicAcademic Publishing and Open Access
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPublishingPublicationIncentiveInfographicScientific publishingElectronic publishingImpact factorDamages

Abstract

fetched live from OpenAlex

Infographic accompanying the "The Drain of Scientific Publishing" Beigel, F., Brockington, D., Crosetto, P., Derrick, G., Fyfe, A., Barreiro, P. G., Hanson, M. A., Haustein, S., Larivière, V., Noe, C., Pinfield, S., & Wilsdon, J. (2025). The Drain of Scientific Publishing [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2511.04820 The four-fold drain of scientific publishing Money: For-profit publishers charge unreasonable fees for reading and publishing that are disconnected from journal production costs. Elsevier, Springer Nature, Wiley and Taylor & Francis made close to $US 15 billion in profit from 2019 to 2024. Time: Researchers spend enormous amounts of time in their roles as authors, reviewers, and editors, to maintain a scholarly publishing system that prioritizes quantity over quality. These time pressures undermine rigor and drive burnout. Trust: Commercial pressures to publish more and faster allow low-quality and fraudulent papers to flood academic journals.The erosion of rigor undermines public confidence in science and damages its credibility. Control: Rankings and metrics like journal impact factor and h-index dictate academic success. Assessment infrastructures are biased towards English journals and controlled by for-profit companies such as Clarivate and Elsevier. Stop the drain: Funders and institution must intervene and change incentives and ownership of scientific publishing. We propose that scholarly publishingneeds to be re-communalized. Universities, libraries, and funders need to build asystem that is communityled and managed. Private organizations may provide services but not extract unreasonable profits. Build on open: The alternative publishing models already exist: preprints, diamond journals, open peer review, publish-review-curate. And so do the open infrastructures: OJS, SciELO, Redalyc, Latindex, African Journals Online, Érudit, etc. We need to align research assessment with open and community led publishing. The visual elements of this infographic were generated with the help of an LLM.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.997
Threshold uncertainty score0.989

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0090.013
Science and technology studies0.0030.003
Scholarly communication0.0250.017
Open science0.0010.006
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.2960.136

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 head0.080
GPT teacher head0.345
Teacher spread0.265 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
DomainEvaluation
GenreOther

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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