The Librarian's Survival Guide to the 'Big Deal': Tools for Unbundling
Bibliographic record
Abstract
At Western University, like many other schools, journal package “big deals” (large, bundled collections of ejournals from the same publisher, purchased at a discount) have been seen as beneficial to the collection based on high discounts and low cost per use. When the Canadian loonie fell to 67 cents on the U.S. dollar in January 2016, it created unexpected financial challenges for collections management. We now had to consider new ways to find cost savings by canceling or unbundling resources, and big deals became a potential target. In evaluating these packages, we looked beyond cost per use, building on work done by the University of Montreal. This paper summarizes the iterative process Western University developed to evaluate and potentially unbundle less valuable big deals. We outline the additional criteria we considered (overlap, current year use, perceived value by faculty members, citation analysis of where our researchers published, and impact factor) and how we made data-driven decisions for unbundling.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.174 | 0.211 |
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".