From Usability Studies to User Experience: Designing Library Services at the University of Kansas
Bibliographic record
Abstract
The University of Kansas (KU) Libraries first made their discovery tool, Primo (Ex Libris), available to their users in the fall of 2013. Since that time, in spite of many upgrades and improvements, most librarians and library staff are still not using the tool for their own research. Last year, librarians from KU presented their findings at the Charleston Conference using a survey given to KU librarians that asked them to compare Primo to Google Scholar and their favorite databases. Librarians were asked to compare the three and make recommendations for improving Primo. This year, KU librarians designed a much briefer survey and asked all library staff to participate, including student assistants. Library staff were asked to use Primo to conduct research on a topic of their choice and use all aspects of Primo to find relevant results. They were then asked to describe what they used in Primo to lead them to helpful information resources and rank the first 10 results from their final search. The purpose of this survey is to discern how our colleagues use Primo and how successful they are in retrieving the information they need when using this search tool. This study will help KU Libraries develop training for library staff in the use of this new mode of discovery and access. The search terms used in this study will also be useful in helping the discovery implementation team recreate the searches to test Primo in the future, after scheduled upgrades, in order to detect noticeable improvements or problems with the search results.
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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 | 0.055 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 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".