Extreme Makeover: How We Decreased Our Collection by 40% and Simultaneously Increased It by 50% in 10 Months
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.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".