Opening the Black Box of Scholarly Communication Funding: A Public Data Infrastructure for Financial Flows in Academic Publishing
Bibliographic record
Abstract
<p class="p1">‘Public access to publicly funded research’ has been one of the rallying calls of the global open access movement. Governments and public institutions around the world have mandated that publications supported by public funding sources should be publicly accessible. Publishers are experimenting with new models to widen access. Yet financial flows underpinning scholarly publishing remain complex and opaque. In this article we present work to trace and reassemble a picture of financial flows around the publication of journals in the UK in the midst of a national shift towards open access. We contend that the current lack of financial transparency around scholarly communication is an obstacle to evidence-based policy-making – leaving researchers, decision-makers and institutions in the dark about the systemic implications of new financial models. We conclude that obtaining a more joined up picture of financial flows is vital as a means for researchers, institutions and others to understand and shape changes to the sociotechnical systems that underpin scholarly communication.
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How this classification was reachedexpand
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 | 0.022 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.013 | 0.025 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.020 | 0.077 |
| Open science | 0.025 | 0.013 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".