Tracking our performance: assessment at the University of Virginia Library
Bibliographic record
Abstract
A library’s infrastructure of programs and personnel is its most valuable asset, providing the foundation for everything it does and aspires to do, which is why assessment is so vitally important. In this collection of case studies, Murphy and her team of contributors describe how quality assessment programs have been implemented and how they are used to continuously improve service at a complete cross-section of institutions. This volume: Looks at how a program was established within a library organization, the individual roles for staff participating in the program, and singles out which activities and projects were most successful Describes programs such as the Baldrige Criteria for Performance Excellence, Lean Six Sigma, and ISO 9001:2000 Examines contexts ranging from a liberal-arts college library and key federal government libraries to libraries that serve major research universities in the United States and Canada Summarizing specific tools for measuring service quality alongside tips for using these tools most effectively, this book helps libraries of all kinds take a programmatic approach to measuring, analyzing, and improving library services.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".