Bibliographic record
Abstract
Purpose The purpose of this paper is to present the findings of an academic library's implementation of a discovery layer (VuFind 1.0 RC1) as a next‐generation catalogue, based on usability testing and an online survey. Design/methodology/approach Usability tests were performed on ten students (eight undergraduates, two graduates), asking a set of 14 task‐oriented questions about the customized VuFind interface. Task completion was scored using a simple formula to generate a percentage indicating success or failure. Changes to the interface were made based on resulting scores and on feedback and observations of users during testing. An online survey was also run for three weeks, to which 75 people responded. The results were analyzed, compared and cross‐tested with the findings of the usability testing. Findings Both the usability testing and survey demonstrated that users preferred VuFind's interface over the classic catalogue. They particularly liked the facets and the richness of the search results listings. Users intuitively understood how to use the deconcatenated Library of Congress Subject Headings. Despite the discovery layer's new functionality, known journal title searching still presents a challenge to users and certain terms used in the interface were problematic. Practical implications It is hoped that the findings will assist implementers of VuFind and other next‐generation catalogues to improve their own systems. The questions add to the body of knowledge about usability testing of library catalogues. Originality/value No previous papers have been published documenting VuFind usability testing. Not only will the findings be relevant, not just to VuFind, but they will also add to the growing body of literature on next‐generation catalogues.
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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 | 0.038 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".