From Focus to Context and Back: Combining Mobile Projectors and Stationary Displays
Bibliographic record
Abstract
Focus plus context displays combine high-resolution detail and lower-resolution overview using displays of different pixel densities. Historically, they employed two fixed-size displays of different resolutions, one embedded within the other. In this paper, we explore focus plus context displays using one or more mobile projectors in combination with a stationary display. The portability of mobile projectors as applied to focus plus context displays contributes in three ways. First, the projector’s projection on the stationary display can transition dynamically from being the focus of one’s interest (i.e. providing a high resolution view when close to the display) to providing context around it (i.e. providing a low resolution view beyond the display’s borders when further away from it). Second, users can dynamically reposition and resize a focal area that matches their interest rather than repositioning all content into a fixed high-resolution area. Third, multiple users can manipulate multiple foci or context areas without interfering with one other. A proof-of-concept implementation illustrates these contributions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".