Implementation of Transformative Agreements at the University of Chicago Library: A Case Study
Bibliographic record
Abstract
The University of Chicago Library created a working group, composed of librarians across the library, to engage in dedicated and focused work around transformative agreements, including understanding how they work and how the library should be engaging with them in a complex open access (OA) landscape. The working group was charged with specific tasks, including determining challenges and opportunities around transformative agreements, developing criteria for determining when to use the library’s OA fund to pursue an agreement, conducting ongoing assessments of the agreements, and developing a set of recommendations to communicate this out to our wider campus community. The group’s work included piloting several transformative agreements and establishing a rubric to evaluate these agreements. The creation of the group allowed the library to gain valuable knowledge and expertise, engage actively in new models for supporting OA, and start critical conversations on campus. The group continues the work, with the ultimate goal of affordable OA publishing and communicating the value of OA with researchers and campus partners, as well as positioning the library as a campus leader in OA.
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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 | 0.040 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.043 | 0.023 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".