The Representation of Library Value in Extra-Institutional Evaluations of University Quality
Bibliographic record
Abstract
The ways in which university quality assessments are developed reveal a great deal about value constructs surrounding higher education. Measures developed and consumed by external stakeholders, in particular, indicate which elements of academia are broadly perceived to be most reflective of quality. This paper examines the historical context of library quality assessment and reviews the literature related to how library value is framed in three forms of external evaluation: accreditation, university rankings, and student surveys. The review finds that the library’s contribution to university quality, when it is considered at all, continues to be measured in terms of collections, spaces, and expenditures, despite significant expansion of library services into non-traditional arenas, including teaching and research, scholarly communications, and data management and visualization. These findings are contrasted with the frequently invoked notion of the library as the heart of the university.
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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 | 0.041 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.023 | 0.016 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".