Scholarly Publishing in the Era of Open Access and Generative Artificial Intelligence
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.038 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.814 | 0.941 |
| Open science | 0.031 | 0.011 |
| Research integrity | 0.000 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it