Visual Keyword/Result Linking: Using Interaction to Dynamically Reveal Relationships
Bibliographic record
Abstract
Keywords contain important contextual information about search results within academic digital library search interfaces. However, such information tends to be underutilized within modern search interface designs. In prior work, methods for visually linking keywords between search results have been proposed and studied. In this research, we analyze the design space and propose a new approach that aggregates the keywords over all items on the search engine results page (SERP), visually linking them back to their source search result. We have created interactive and static versions of both interfaces, and conducted a controlled laboratory study to assess the impact of the interfaces on measures of utility (efficiency, effectiveness, feature use) and perceived value (usefulness, ease of use, satisfaction, user engagement, knowledge gain, and interest gain). The findings from this research show the merit of using keywords to provide summaries of documents and search result sets, the value of making keywords interactive, and the benefit of using visualization to interactively link information within a search engine results page. The differences between providing the keywords along side each document or aggregated over the entire SERP were minimal, suggesting that it does not matter how the keywords are represented as long as they can be used to interactively reveal relationships among 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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 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".