The Buyback Dilemma: How We Developed a Principle-Based, Data-Driven Approach to Unbundling Big Deals
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
University of Saskatchewan is a publicly funded, medium-sized research intensive medical doctoral university in Canada. Like other academic libraries, we have been coping with the rising costs of Big Deal journal packages in the context of shrinking budgets and variable currency fluctuation between the Canadian and American Dollar. When faced with a need to cancel two Big Deal packages in order to balance our budget, we undertook a data-driven, principles-based approach. We discuss the context at University of Saskatchewan, and the principles and steps we used to successfully determine which packages to cancel, and how to determine titles for re-subscription within a limited budget. We discuss how we compiled and used data that addresses scholarly (citation), pedagogical (downloads), and reputational (survey responses) concerns, and share the formula we developed. We also share some lessons learned and recommendations and ideas for future Big Deal assessment.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| 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