Creating a Culture of Meaningful Evaluation in Public Libraries: Moving Beyond Quantitative Metrics
Bibliographic record
Abstract
The current state of practice sees public libraries, like all public institutions, enduring funding challenges within the dominant political-economic environment, which is shaped by the tenets of new public management and the neoliberal audit society. Libraries, feeling threatened and unsure about their future stability, seek new ways to demonstrate their value. However, they face institutional cultural constraints when attempting to introduce new assessment methods to meet this challenge. The new dynamics require them to go beyond output measures (counts). With research findings supported by survey and interview data from Ontario public libraries, and in agreement with the current literature on the subject, we propose a new model to address this phenomenon, serving two purposes: demonstrating a library’s present state of cultural readiness to introduce new systems of outcome assessment and charting a path toward creating a culture of meaningful evaluation.
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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 | 0.297 | 0.230 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.022 | 0.098 |
| Scholarly communication | 0.067 | 0.033 |
| Open science | 0.005 | 0.035 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".