Evaluating Visual Analytics for Relevant Information Retrieval in Document Collections
Bibliographic record
Abstract
Abstract Retrieving information from document collections is necessary in many contexts, e.g. researchers search for papers on a topic, physicians search for records of patients with a certain condition and police investigators seek relationships between different criminal reports. Finding relevant textual content in a corpus can be challenging in scenarios where the users expect a retrieval process with high recall. Visual Analytics (VA) systems that integrate interactive visualizations and machine learning algorithms are often advocated to support retrieval tasks in such complex scenarios. However, few studies report an end-user perspective on the utility of such systems. We present results from observational studies on VA-supported information retrieval conducted with graduate students and researchers using a system to explore collections of scientific papers. While users have, in general, positive views of the system’s potential to facilitate their retrieval tasks, some faced practical difficulties in using it effectively, and we found considerable variation in their assessment of specific functionalities. Our findings reinforce the potential of VA systems and also the importance of carefully informing users of the underlying conceptual models in such systems and their limitations.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".