Investigating device‐specific visual feedback for cross‐device transfer in table‐centric multisurface environments
Bibliographic record
Abstract
Summary Table‐centric multisurface environments (T‐MSEs) that combine small multitouch surfaces (eg, smartphones and tablets) with large interactive tabletops provide people with both personal and shared workspaces to support various independent and collective tasks during group activities. This paper reports on the third in a series of studies exploring how existing interaction methods for cross‐device transfer, such as the Pick‐and‐Drop (P&D) method, can be adapted to table‐centric multisurface environment settings. The study examined the use of device‐specific visual feedback to improve users' awareness of transferred content during P&D transfer. The tabletop feedback utilized the existing Surface Ghosts P&D feedback approach (ie, “ghosted” versions of transferred content were displayed in real time under the user's hand). The tablet feedback consisted of a static “Tablet Bridge” feedback showing miniature versions of transferred content along the top edge of the tablet interface. The study found that providing both types of feedback significantly improved users' transfer awareness over providing Surface Ghosts feedback alone. It also revealed that the Tablet Bridge feedback helped compensate for technical and usability issues associated with the Surface Ghosts feedback design. Lessons learned from our combined series of cross‐device transfer studies are reflected upon, and relevant design implications are discussed.
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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 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".