Drag-and-Drop Query Refinement and Query History Visualization for Mobile Exploratory Search
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Conducting exploratory searches within digital libraries requires that searchers revise, refine, and reformulate their queries multiple times. Challenges that searchers of digital public libraries face include choosing how to refine their queries and making spelling or typographical errors. These are compounded when using mobile devices, where typing is time-consuming and error-prone. Conducting searches in a mobile context adds yet another challenge: the possibility of being interrupted and losing track of what was being done. In this paper we demonstrate a novel digital public library search interface tuned for mobile device use, which was designed to address these challenges through two key features: drag-and-drop query refinement and query history visualization. This work represents an example of how thoughtful search interface design and the judicious use of visualization techniques can be used to enhance exploratory search processes within digital public libraries.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it