Scholarly Publishing in the Era of Open Access and Generative Artificial Intelligence
Bibliographic record
Abstract
Tensions around open-access initiatives and the democratization of scholarly content are intensifying, particularly with the rise of generative artificial intelligence (AI). Barriers to open-access publishing are becoming more pronounced across regions, especially in the global South, but there are actionable strategies to reduce these challenges and enhance accessibility. Generative AI plays a dual role in scholarly publishing, boosting content creation and quality assurance while also raising concerns about workforce reductions. Collaboration among publishers, researchers, libraries, technologists, and policymakers is essential to addressing critical issues like research integrity, funding shortages, intellectual property conflicts, and growing inequities in publishing. Despite these challenges, the digital landscape offers new opportunities for building a more equitable and sustainable knowledge ecosystem, pushing for re-evaluating current practices to shape the future of publishing and innovation.
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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 | 0.038 | 0.115 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.023 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.056 | 0.044 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.034 | 0.012 |
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".