Using UX Testing to Optimize Discoverability of Non-traditional Resources
Bibliographic record
Abstract
Objective – The accessibility of non-traditional resources presents ongoing challenges for users and librarians. This study investigates methods for optimizing metadata and the placement of search results to enhance the discoverability of these resources within library systems. Researchers conducted A/B testing to compare two features of Ex Libris Primo: the Resource Recommender and Discovery Import Profiles. The objective was to enhance user access to a broader range of informational assets beyond conventional collections. This study posed the research question: Is inclusion in the results list (Discovery Import Profiles) or are visually appealing advertisement-style cards above results (Resource Recommender) a more effective method for discovery of non-traditional library resources? Methods – Researchers identified four key resource types for testing: librarians, frequently asked questions (FAQs), databases, and research guides. An A/B test was conducted with each resource presented in the Discovery Import Profiles and Resource Recommender formats. Following the A/B test, a combined C test was conducted to validate findings. Results – The ad-style cards achieved higher engagement rates, particularly for databases and FAQs, while research guides performed better when embedded directly in search results. This study highlights the strengths and limitations of each method. Databases and FAQs benefited from the visual prominence of the ad-style cards, while research guides were more discoverable within search results. However, minimal engagement with librarians as a resource type across both methods suggests the need for improved tagging and metadata strategies. Conclusion – Findings underscore the importance of institution-specific research and localized assessments to ensure effective implementation of discovery strategies. This study provides a useful method for libraries aiming to enhance the discoverability of their non-traditional resources, ultimately improving user access and satisfaction.
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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 | 0.060 | 0.248 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".