Visualizing search results: evaluating an iconic visualization
Bibliographic record
Abstract
Commercial websites offer many items to potential site users. However, most current websites display results of a search in text lists, or as lists sorted on one or two single criteria. Finding the best item in a text list based on multi-priority criteria is an exhausting task, especially for long lists. Visualizing search results and enabling users to perceive the tradeoffs among the results based on multiple priorities may ease this process. To investigate this, two different techniques for displaying and sorting search results are studied in this paper; Text, and XY Iconic Visualization. The goal is to determine which technique for representing search results would be the most efficient one for a website user. We conducted a user study to compare the usability of the two techniques. Collected data is in the form of participants' task responses, a satisfaction questionnaire, qualitative observations, and participants' comments. According to the results, iconic visualization is better for overview (it gives a good overview in a short amount of time) and search with more than two criteria, while text-based performs better for displaying details.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 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".