Canceling the Big Deal: Three R1 Libraries Compare Data, Communication, and Strategies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Canceling the Big Deal is becoming more common, but there are still many unanswered questions about the impact of this change and the fundamental shift in the library collections model that it represents. Institutions like Southern Illinois University Carbondale and the University of Oregon were some of the first institutions to have written about their own experience with canceling the Big Deal several years ago, but are those experiences the norm in terms of changes in budgets, collection development, and interlibrary loan activity? Within the context of the University of California system’s move to cancel a system-wide contract with Elsevier, how are libraries managing the communication about Big Deals both internally with library personnel as well as externally with campus stakeholders? Three R1 libraries (University of Maryland, University of Oklahoma, and Kansas State University) will compare their data, discuss both internal and external communication strategies, and examine the impact these decisions have had on their collections in terms of interlibrary loan and collection development strategies. The results of a brief survey measuring the status of the audience members with respect to Big Deals, communication efforts with campus stakeholders, and impacts on collections will also be discussed.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it