How assessment websites of academic libraries convey information and show value
Bibliographic record
Abstract
Purpose As libraries are required to become more accountable and demonstrate that they are meeting performance metrics, an assessment website can be a means for providing data for evidence-based decision making and an important indicator of how a library interacts with its constituents. The purpose of this paper is to share the results of a review of websites of academic libraries from four countries, including the UK, Canada, Australia and the USA. Design/methodology/approach The academic library websites included in the sample were selected from the Canadian Association of Research Libraries, Research Libraries of the United Kingdom, Council of Australian University Libraries, Historically Black College & Universities Library Alliance, Association of Research Libraries and American Indian Higher Education Consortium. The websites were evaluated according to the absence or presence of nine predetermined characteristics related to assessment. Findings It was discovered that “one size does not fit all” and found several innovative ways institutions are listening to their constituents and making improvements to help users succeed in their academic studies, research and creative endeavors. Research limitations/implications Only a sample of academic libraries from each of the four countries were analyzed. Additionally, some of the academic libraries were using password protected intranets unavailable for public access. The influences of institutional history and country-specific practices also became compelling factors during the analysis. Originality/value This paper seeks to broaden the factors for what is thought of as academic library assessment with the addition of qualitative and contextual considerations.
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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 | 0.029 | 0.191 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.020 | 0.018 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".