Eliciting usable gestures for multi-display environments
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 (MDEs) have advanced rapidly in recent years, incorporating multi-touch tabletops, tablets, wall displays and even position tracking systems. Designers have proposed a variety of interesting gestures for use in an MDE, some of which involve a user moving their hands, arms, body or even a device itself. These gestures are often used as part of interactions to move data between the various components of an MDE, which is a longstanding research problem. But designers, not users, have created most of these gestures and concerns over implementation issues such as recognition may have influenced their design. We performed a user study to elicit these gestures directly from users, but found a low level of convergence among the gestures produced. This lack of agreement is important and we discuss its possible causes and the implication it has for designers. To assist designers, we present the most prevalent gestures and some of the underlying conceptual themes behind them. We also provide analysis of how certain factors such as distance and device type impact the choice of gestures and discuss how to apply them to real-world systems.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it