Extreme Makeover: How We Decreased Our Collection by 40% and Simultaneously Increased It by 50% in 10 Months
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
The Brennan Library at Lasell College had not conducted a systematic weeding in over 20 years. With space in demand and an increase in online courses, desperate times called for drastic measures. Over a 10-month period, the library withdrew 40% of its tangible collections. Simultaneously, the staff’s focus shifted to promoting e-resources and adopting the EBSCO EDS discovery layer. Using a weighted collection development allocation formula, the librarians overhauled the materials budget and designed a departmental liaison program. After calculating the holdings of new e-book and streaming video packages, the library’s collection increased by 50% despite the massive deaccessioning. This paper describes how a small academic library with limited funds and staffing made major changes leading to positive perceptions and avoiding imposing threats. The Brennan Library added seating, zoned areas, and in-demand e-resources for a growing distance-learner population. By changing the collection development emphasis from just-in-case to just-in-time, the library now provides access to more items than ever before. The Brennan Library’s example illustrates that an access over ownership model of acquisitions can give similar libraries improved return on investment and positive improvements for stakeholders, provided that significant changes are communicated in a strategic manner emphasizing benefits for the user community.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| 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