An Exploratory Study of Tag-based Visual Interfaces for Searching Folksonomies
Bibliographic record
Abstract
Aesthetic features such as animation, 3D interaction, and visual metaphors are becoming commonplace in multimedia search interfaces. However, it is unclear which attributes are needed to encourage people to use these interfaces on an ongoing basis. To design a visual interface that will elicit continual use, we first need to establish a better understanding of users’ goals and strategies, in order to determine which features are critical to support those tasks. This paper reports on an exploratory study of individuals engaging with five different image and video search interfaces. Our study helped us to understand users’ experiences with a variety of features and design elements, as well as categorize their common search tasks and strategies. We identified four distinct types of search: Search Known Objects + Known Keywords, Search Known Objects + Unknown Keywords, Search Unknown Objects + Known Keywords, and Search Unknown Objects + Unknown Keywords. We also identified common strategies used to accomplish each of these search types. Our findings suggest that search interfaces should maximize screen space used for visual representations of the media, provide on-demand access to titles, tags, and other metadata, and provide contextual information about previously viewed items, current keywords, and alternate keyword possibilities.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".