A study of search result aggregation approaches for the digital humanities
Bibliographic record
Abstract
Abstract Searching across diverse information platforms, such as digital humanities archives, academic digital libraries, and encyclopedias, poses challenges in managing the queries issued to each platform and synthesizing the resources discovered. While search result aggregation interfaces address this problem, how best to present the search results from different platforms in the search engine results page remains an open question. In this research, we implemented three common approaches and developed a new technique for aggregating search results across three platforms: Europeana, our University's academic library, and Wikipedia. The three common approaches (1) use tabs to switch between the platforms, (2) interleave results from each platform producing a single list, and (3) use a bento box approach to group results from each platform. The new technique organizes the search results into thematic clusters irrespective of their source platform. We designed a controlled laboratory study using a within‐subjects design and exploratory search tasks conducted in the context of digital humanities searching. We collected data from 32 student participants, focusing on utility, perceived value, and diversity of saved resources. This study provides evidence that thematic clustering can be a beneficial aggregation approach, opening opportunities for studying different ways of representing and visualizing aggregated 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 | 0.023 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".