There and Back Again: Cross-Display Object Movement in Multi-Display Environments
Bibliographic record
Abstract
Multi-display environments (MDEs) are now becoming common, and are becoming more complex, with more displays and more types of display in the environment. One crucial requirement specific to MDEs is that users must be able to move objects from one display to another; this cross-display movement is a frequent and fundamental part of interaction in any application that spans two or more display surfaces. Although many cross-display movement techniques exist, the differences between MDEs—the number, location, and mixed orientation of displays, and the characteristics of the task they are being designed for—require that interaction techniques be chosen carefully to match the constraints of the particular environment. As a way to facilitate interaction design in MDEs, we present a taxonomy that classifies cross-display object movement techniques according to three dimensions: the referential domain that determines how displays are selected, the relationship of the input space to the display configuration, and the control paradigm for executing the movement. These dimensions are based on a descriptive model of the task of cross-display object movement. The taxonomy also provides an analysis of current research that designers and researchers can use to understand the differences between categories of interaction techniques.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".