Canceling the Big Deal: Three R1 Libraries Compare Data, Communication, and Strategies
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.
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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 | 0.032 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.023 | 0.014 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".