Adding SPICE to a Library Intranet Site: A Recipe to Enhance Usability
Bibliographic record
Abstract
Objective - To produce a highly-usable intranet site, use the project to explore the practical application of evidence-based librarianship (EBL), and refine the library’s project management methodology. Methods - Evidence was gathered via a literature review, an online survey, scenario-based usability testing, and completion of a usability checklist. Usability issues were then addressed, guided by the Research-Based Web Design and Usability Guidelines. Results - After a preliminary revision, the site achieved a usability index of 79% after application of the “Raward Library Usability Analysis Tool”. Finding the information and supporting user tasks were identified as areas of weakness. Usability testing and client feedback supported these findings. After these issues were addressed by a major site redevelopment, the usability index increased to 98%. Conclusions - Raward’s checklist is an easy and effective tool for measuring and identifying usability issues. Its value was enhanced by scenario-based usability testing, which yielded rich, client-specific information. The application of EBL and project management principles enhanced the outcomes of the project, and the professional development of the project team.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.010 |
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".