Monitoring the transition to open access: Jisc-Wiley transitional agreement
Bibliographic record
Abstract
The Jisc-Wiley Read and Publish agreement enables researchers from participating UK institutions to publish their articles immediately as open access (OA) in over 1,400 hybrid open access journals and 230+ fully open access journals as well as access content published in 1,480+ subscription journals. It aims to reduce cost and administration barriers by allowing all UK articles published in Wiley open access and subscription journals to be made open access immediately upon publication. This assists institutions with compliance for existing funder requirements and reduces the burden of having to re-allocate funds from research grants to pay for article publication charges (APC). The Jisc-Wiley Read and Publish agreement has three core aims: \n \n1. To contain the costs of publication and subscription access for institutions \n2. To reduce cost and administration barriers to hybrid open access publishing and support \ncompliance with UK funder policies \n3. To increase the number of open access articles \n \nWe have analyzed the first year’s data of the Jisc-Wiley four-year Read and Publish agreement against these aims including whether it has had a positive impact on increasing the ratio of UK open access research output in 2020. This includes exploring consortium level, institutional level and article level data to determine the agreement’s performance.
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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 | 0.190 | 0.484 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.029 | 0.015 |
| Open science | 0.007 | 0.021 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.023 | 0.016 |
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".