Enabling Exploratory Browsing using Dynamic Search Result Tagging, Highlighting, and Filtering
Bibliographic record
Abstract
In academic digital libraries, searchers commonly engage in exploratory search when faced with complex search tasks. An important part of exploratory search is exploratory browsing, where the focus is on search activities associated with discovery, learning, and investigation. However, these critical aspects of exploratory browsing are often not adequately supported by existing digital library search systems. In particular, they are hindered by the inability for searchers to add further information to inform their exploratory browsing style of searching. We address this issue by providing two new features: dynamic tagging of search results and an interactive workspace that allows the searcher to highlight and filter the search results using these tags. We have evaluated this approach compared to a baseline search system in a 32-participant user study. Increases in typical subjective measures were found, along with increases in perceived motivation and ability. Further, the documents saved as part of the exploratory browsing process were of higher precision when using this approach. These results show the value of providing searchers with interactive features that enable an exploratory browsing style of searching, beyond simply entering a query and selecting/saving search results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.019 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".