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
In this paper, we present an attentive windowing technique that uses eye tracking, rather than manual pointing, for focus window selection. We evaluated the performance of 4 focus selection techniques: eye tracking with key activation, eye tracking with automatic activation, mouse and hotkeys in a typing task with many open windows. We also evaluated a zooming windowing technique designed specifically for eye-based control, comparing its performance to that of a stan-dard tiled windowing environment. Results indicated that eye tracking with automatic activation was, on average, about twice as fast as mouse and hotkeys. Eye tracking with key activation was about 72% faster than manual conditions, and preferred by most participants. We believe eye input performed well because it allows manual input to be provided in parallel to focus selection tasks. Results also suggested that zooming windows outperform static tiled windows by about 30%. Furthermore, this performance gain scaled with the number of windows used. We conclude that eye-controlled zooming windows with key activation pro-vides an efficient and effective alternative to current focus window selection techniques.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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