Are Scholars' Publishing Choices Fueling the Crisis in Scholarly Journal Publishing?
Bibliographic record
Abstract
Academic publishing is in crisis. The prominence of commercial publishers has made the laudable goal of open access (OA) unaffordable through expensive article processing charges (APCs). Even as institutions offset costs through “read and publish” agreements, academic publishing models remain a drain on the research enterprise. Simply put, the costs to publish, OA or otherwise, are not sustainable. While commercial publisher profits are a central problem, scholars also share responsibility. Academia rewards publishing in high-cost, prestigious journals, reinforcing commercial dominance. Scholars’ publishing choices help sustain a reward system that prioritizes impact metrics over access, equity, and knowledge as a public good. Choosing to publish in commercial journals reinforces their dominance. Fortunately, there are alternatives. Diamond open access offers a community-owned, non-commercial model with no fees for authors or readers. These journals, often supported by academic institutions, libraries, or scholarly communities, prioritize equity, openness, and scholar-led governance. Open Journal Systems (OJS), a community-owned, open-source platform, has been instrumental in enabling this model. Used by over 55,000 journals worldwide, OJS supports about 60% of all diamond OA journals globally. This widespread adoption demonstrates not only the scalability and sustainability of the platform, but also the global appetite for an inclusive, non-commercial alternative to legacy publishing models. In this lightning talk, we’ll use data about journals using OJS to show the breadth of diamond OA journals available to researchers across disciplines, and why researchers' choices matter. This is a call to action to change our publishing practices: from supporting models that extract, to systems that empower.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.146 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.021 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.046 | 0.047 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".