Bibliographic record
Abstract
Visualization research generates beautiful images and impressive interactive systems. Emphasis on evaluating visualizations is growing. Researchers have successfully used alternative evaluation techniques in human-computer interaction (HCI), including focus groups, field studies, and expert reviews. These methods tend to produce qualitative results and require fewer participants than controlled experiments. In this article, we focus on expert reviews that we used for the applications. We commonly use expert reviews to assess interface usability. Expert reviews can generate valuable feedback on visualization tools. We recommend i) including experts with experience in data display as well as usability, and ii) developing heuristics based on visualization guidelines as well as usability guidelines. Expert reviews should not be used exclusively, since experts might not hilly predict end-user actions. Furthermore, we encourage more experimentation with this technique, particularly to develop a good set of visualization heuristics and to compare it with other methods.
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 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 | 0.284 | 0.753 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".