Big Deal Cancellations and Influences on Librarian Decision-Making
Bibliographic record
Abstract
Big Deals initially emerged as cost-saving purchasing models through which academic libraries could quickly grow their collections. Over time, the soaring costs of journal bundles have strained library budgets, and librarians have worked to transition away from Big Deals. Cancellation projects are complex processes involving a large amount of time and labour. Past research has examined how librarians use quantitative and/or qualitative data to make decisions around cancellations, but few go inside the process to understand the subjective factors influencing librarians’ choices. This study investigates the decision-making practices and processes of librarians concerned with the cancellation of Big Deals through interviews conducted at four medium-sized Canadian institutions that underwent cancellation projects from 2015 to 2020. The institutions investigated in this study adopted similar practices in deciding what packages to unbundle and selecting their teams. Differences in how qualitative and quantitative data were used in forming analyses, and the communication methods to counteract opposition heavily influenced the relative success of each library. Libraries seemed most successful if they could perform nuanced and complex data analyses, involved their Liaison librarians in faculty consultations, had the strong support of administrators, and wrapped the project together with an integrated communications plan. A model describing the decision-making steps in the process of unbundling journal packages and the influences that impact each step is presented, followed by recommendations for engaging with each influencing factor, based on the findings of this study.
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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 | 0.027 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".