Scoping Review of Transformative Agreement Research
Bibliographic record
Abstract
Objective – Transformative agreements (TAs) are agreements between publishers and institutions or consortia that combine reading access and open access (OA) publishing. They can take many forms, and the first agreement is believed to have started in 2014. This scoping review aims to identify and synthesize the existing research on TAs. Methods – Following benchmarking and term harvesting, electronic searches were conducted in 48 databases and were complemented with handsearching and citation chaining which resulted in 1843 unique results. Results were screened with pre-registered inclusion and exclusion criteria which resulted in inclusion of 151 studies (80 case studies, 39 quantitative, 31 qualitative, and 1 theoretical). Results – The heterogeneity of methods and findings of research on TAs made synthesis challenging. The synthesis was further complicated by the corpus including studies examining different time periods, publishers, agreement types, participating institutions, and more. Studies had varied intended audiences and research dissemination routes further complicating discovery and synthesis. Conclusions – Despite the heterogeneity, some themes emerged, including TAs increase hybrid OA and that consortia can play an important role in negotiating and managing TAs. Successful implementation relies on a number of factors, including workflows for authors and those managing the agreements. Studies found that TAs are not leading to a transformation of the publishing system as a whole.
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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 | 0.074 | 0.255 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.049 | 0.046 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".