Bibliographic record
Abstract
Purpose While originally designed for the for‐profit sector, the Balanced Scorecard has been adopted by non‐profit and government organizations, including some libraries. This paper aims to focus on the continued experiences of two prominent North American research libraries, Johns Hopkins University and McMaster University. These two libraries were part of an Association of Research Libraries (ARL) pilot effort that included a total of four institutions, the two represented by the authors, plus the University of Virginia and the University of Washington. Design/methodology/approach The authors use a combination of quantitative and qualitative approaches. The quantitative aspects of the study are informal and theme‐based. When examining commonalities between Scorecards or overlap between Scorecard measures and the ARL statistics program, matches are made based on broad themes regardless of the specific words used in the formulae. Findings The participating libraries identified ten commonly measured “themes.” These themes are defined as key areas of focus present in three out of the four local sites. Using the standardized four‐perspective Scorecard framework, these themes are as follows: the customer – quality of physical space, customer satisfaction, instruction, document delivery, and collection preservation/discovery; financial health – revenue generation; learning and growth – employee satisfaction and diversity; internal processes – library promotion and assessment of services. Originality/value The article explores the question; can libraries improve their arsenal of assessment tools by working alongside each other (as opposed to directly with each other) as they implement local organizational performance measurement instruments?
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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 | 0.036 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.020 | 0.010 |
| Open science | 0.006 | 0.022 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.069 | 0.039 |
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".