The Unadorned Desk: Exploiting the Physical Space around a Display as an Input Canvas
Bibliographic record
Abstract
In everyday office work, people smoothly use the space on their physical desks to work with documents of interest, and to keep associated tools and materials nearby for easy use. In contrast, the limited screen space of computer displays imposes interface constraints. Associated material is either placed off-screen (i.e., temporarily hidden) and requires extra work to access (window switching, menu selection) or crowds and competes with the work area (e.g., as palettes and icons). This problem is worsened by the increasing popularity of small displays such as tablets and laptops. To mitigate this problem, we investigate how we can exploit an unadorned physical desk space as an additional input canvas. Our Unadorned Desk detects coarse hovering over and touching of areas on an otherwise standard physical desk, which is used as input to the desktop computer. Unlike other augmented desks, feedback is given on the computer’s screen instead of on the desk itself. To better understand how people make use of this new input space, we conducted two user studies: (1) placing and retrieving application icons onto the desk, and (2) retrieving items from a predefined grid. We found that participants organize items in a grid for easier access, and are generally faster without affecting accuracy without on-screen feedback for few items, but were more accurate (though slower as they relied on feedback) for many items
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".