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
Visualizing information in user interfaces to complex, large-scale systems is difficult due to visual fragmentation caused by an enormous amount of inter-related data distributed across multiple views. New display dimensions are required to help the user visually integrate and filter such spatially distributed and heterogeneous information. Motion holds promise in this regard as a perceptually efficient display dimension. It has long been known to have a strong grouping effect, suggesting it has potential for filtering and brushing techniques. However, there is little known about which properties of motion are most effective. This paper reviews the prior literature relating to the use of motion for display and discusses the requirements for how motion can be usefully applied to these problems, especially for visualizations incorporating multiple groups of data objects. Results from previous research by the authors suggested motion type was a strong distinguishing feature. Three types of motions in pairwise combinations were compared: linear, circular and expansion/contraction. Combinations of linear directions were also compared to evaluate how great angular separation needs to be to enforce perceptual distinction. The results showed that motion can effectively group objects that are otherwise dissimilar. Type differentiation is more effective than directional differences (except for 90°). Of the three types studied, circular demands the most attention. Angular separation must be 90° to be equally effective. These results suggest that motion can be usefully applied to both filtering and brushing. They also provide the beginnings of a vocabulary of simple motions that can be applied to information visualization.
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 | 0.001 | 0.006 |
| 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