Bibliographic record
Abstract
Multi-monitor displays and multi-display environments are now common. Cross-display cursor movement, in which a user moves the pointer from one display to another, occurs frequently in these settings. There are several techniques for supporting this kind of movement, and these differ in the way that they deal with displayless space (the physical space between displays). Stitching is the method used by most operating systems; in this technique, the cursor jumps from the edge of one display directly into the next display. In contrast, Mouse Ether maps the motor space of the mouse exactly to the physical space of the displays, meaning that the cursor has to travel across displayless space until it reaches the next display. To determine which of these approaches is best for cross-display movement, we carried out a study comparing Stitching, Mouse Ether, and a variant of Mouse Ether with Halo for off-screen feedback. We found that Stitching is equivalent to or faster than any variant of Mouse Ether, and that Halo improves Ether’s performance (but not enough to outperform Stitching). Results also indicate that the larger the gap between displays, the longer the targeting takes – even for Stitching. These findings provide valuable guidance for practitioners and raise new interesting questions for research.
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 | 0.001 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".