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
Multi-display environments and smart meeting rooms are now becoming more common. These environments build a shared display space from variety of devices: tablets, projected surfaces, tabletops, and traditional monitors. Since the different display surfaces are usually not organized in a single plane, traditional schemes for stitching the displays together can cause problems for interaction. However, there is a more natural way to compose display space -- using perspective. In this paper, we develop interaction techniques for multi-display environments that are based on the user's perspective on the room. We designed the Perspective Cursor, a mapping of cursor to display space that appears natural and logical from wherever the user is located. We conducted an experiment to compare two perspective-based techniques, the Perspective Cursor and a beam-based technique, with traditional stitched displays. We found that both perspective techniques were significantly faster for targeting tasks than the traditional technique, and that Perspective Cursor was the most preferred method. Our results show that integrating perspective into the design of multi-display environments can substantially improve performance.
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