Improving revisitation in fisheye views with visit wear
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
The distortion caused by an interactive fisheye lens can make it difficult for people to remember items and locations in the data space. In this paper we introduce the idea of visit wear - a visual representation of the places that the user has previously visited - as a way to improve navigation in spaces affected by distortion. We outline the design dimensions of visit wear, and report on two studies. The first shows that increasing the distortion of a fisheye view does significantly reduce people's ability to remember object locations. The second study looks at the effects of visit wear on performance in revisitation tasks, and shows that both completion time and error rates are significantly improved when visit wear is present. Visit wear works by changing the revisitation problem from one of memory to one of visual search. Although there are limitations to the technique, visit wear has the potential to substantially improve the usability both of fisheye views and of graphical information spaces more generally.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 it