Impact of Transformative Agreements on Publication Patterns
Bibliographic record
Abstract
"Transformative agreements" are agreements made between publishers and institutions that were intended to transform the traditional subscription-based scholarly publishing system to open access. Some publishers and institutions have argued that these are the best option, yet, they are increasingly being called into question. Not only does the transition remain incomplete, they create negative effects on researchers without access to an agreement or funding to pay an article processing charge. This research project sought to address the question of whether transformative agreements increase the number of open access publications. In April 2022, we retrieved 370 transformative agreements from the ESAC Transformative Agreement Registry, of which 72 met our inclusion criteria. At that time, agreements in the ESAC Registry were heavily weighted towards Europe. We retrieved publications from the Web of Science Core Collection, and screened these to ensure that they were authored by researchers at participating institutions and published in hybrid open access journals covered by the agreement. Using the Unpaywall API, we determined the open access status of each item. Through this process, we identified 156,053 publications that met inclusion criteria. In this article, we examine changes in publication patterns at an aggregate level and per agreement.
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.
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 | 0.041 | 0.284 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.030 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".