Making Collection Management Manageable: A Three-Phase Approach to an Annual Subscription Review
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
Annual subscription reviews are a normal part of many libraries’ operations, but this process is time consuming and can be particularly challenging for institutions with small e-resources staffs. The approach pursued by the Michael Schwartz Library at Cleveland State University includes strategies other libraries may find helpful in moving beyond cost per use in their reviews. In early fiscal year 2019, the Michael Schwartz Library identified a need to systematically review all subscriptions annually. The library operates with a flat budget and cancellations are often required to manage inflation. Previously, subscription reviews were in response to immediate needs (e.g. budget cuts, changes in consortium offerings, etc.). Largely due to staffing and time constraints, examining the entire corpus of subscriptions was outside of the scope of past reviews. A new subscription review process was developed to prepare the library to make data-driven decisions regarding cancellations for the next fiscal year. The methodology developed for the new subscription review consisted of three phases with each phase narrowing the number of resources considered for cancellation. The first phase was an evaluation of resource performance from an acquisitions perspective and incorporated cost per use and annual price increases. In the next phase, subject librarians evaluated resources in their respective disciplines based on several criteria and were required to rank resources in order of retention priority. In the final phase, faculty were surveyed on content quality, frequency of use in instruction, and other criteria for those resources deemed “cancellation eligible.”
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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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