Scaling up open access publishing through transformative agreements: results from 2019 to 2022
Bibliographic record
Abstract
The Biochemical Society provides a comparative case study showing the results of the transitioning of its journals to open access (OA) in three key publishing regions: 1. the UK and Australasia, 2. USA and Canada and 3. China. We wanted to test our theory that institutionally-funded OA through transformative agreements (TAs) delivers sustainable growth more successfully than author-funded OA via article publishing charges (APCs). Over a 4-year period from 2019 to 2022 we were able to chart the effects of different national and institutional levels of support for OA. Through concerted and strategic action at a national level by library consortia groups – Jisc in the UK, and CAUL in Australia and New Zealand – OA has shifted to become the predominant route of publication in this region of our study. Our data indicate that North America, behind this curve by a few years, is moving in a similar direction with higher uptake of OA in 2022. In contrast, OA publishing from China which at the start of the study represented the region with the highest OA output across our portfolio shows a dramatic decline. We believe this volatility may be a result of a lack of OA policy guidance, overreliance on the APC model, academic malpractice, as well as a lack of TAs which might otherwise support a more stable and diverse OA publishing output.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".