A study of search result aggregation approaches for the digital humanities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it