Exploratory Search in Digital Humanities: A Study of Visual Keyword/Result Linking
Bibliographic record
Abstract
ABSTRACT While searching within digital humanities collections is an important aspect of digital humanities research, the search features provided are usually more suited to lookup search than exploratory search. This limits the ability of digital humanities scholars to undertake complex search scenarios. Drawing upon recent studies on supporting exploratory search in academic digital libraries, we implemented two visual keyword/result linking approaches for searching within the Europeana collection; one that keeps the keywords linked to the search results and another that aggregates the keywords over the search result set. Using a controlled laboratory study, we assessed these approaches in comparison to the existing Europeana search mechanisms. We found that both visual keyword/result linking approaches were improvements over the baseline, with some differences between the new approaches that were dependent on the stage of the exploratory search process. This work illustrates the value of providing advanced search functionality within digital humanities collections to support exploratory search processes, and the need for further design and study of digital humanities search tools that support complex search scenarios.
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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 | 0.029 | 0.273 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".