An Academic Library’s Efforts to Justify Materials Budget Expenditures
Bibliographic record
Abstract
Academic libraries, like the universities and colleges they serve, are facing increasing pressures to justify budgets and expenditures.Using the business model employed at several other research institutions, the University of Florida (UF) has adopted the accounting system Responsibility Center Management (RCM) which necessitates the university's sixteen colleges to track their individual operational budgets including absorbing a revised tax levied to finance the library.This tax has created a renewed sense of urgency for the library to show details of the material budget expenditures for each college.This paper reveals how staff in the UF Library's Acquisitions Department developed a fresh mapping strategy to track costs of the traditional book budget, print serials, and other tangible materials, but also expenditures for all e-resources drilled down to the individual e-journals purchased through Big Deal packages.Going forward, the library can use this refashioned budget system to reallocate its materials budget to more accurately support the colleges of UF.
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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 | 0.015 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.026 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.009 |
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".