Tracking our performance: assessment at the University of Virginia Library
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
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.
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 | 0.000 | 0.008 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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