Next Steps in Discovery Implementation: User‐Centered Discovery System Redesign
Bibliographic record
Abstract
This paper will discuss a discovery system redesign project at the University of Houston Libraries, and in particular the Discovery Redesign Team’s collaborative, user‐centered approach. Throughout the redesign process, the team collected information about the needs and expectations of internal and external users regarding the Library’s discovery system. The team worked with two internal working groups to gather and evaluate the collected information. The results of this evaluation were used to make user‐centered design decisions. The Discovery Redesign Team worked with the Discovery Advisory Group, made up of library employees from various departments, to seek feedback and suggestions throughout the redesign process. Working collaboratively with this Group informed design decisions made by the team while also generating buy‐in for the discovery redesign. The team worked with the Discovery Usability Group to collect information from end‐users to inform the Team’s design decisions. The Committee held focus groups with the Library Information Desk staff to learn how the discovery system was serving users, and where it was falling short; they conducted usability tests with students to find out where users were experiencing breakdowns while completing common tasks. The methodologies and findings of the team’s various activities will be discussed. Changes to system interfaces affect both internal and external users. The University of Houston’s discovery system redesign is an example of a successful, user‐centered, collaborative design project.
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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 | 0.109 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".