Bridging Private and Shared Interaction Surfaces in Collocated Groupware.
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 (such as the pairing of a digital tabletop computer with a set of handheld tablet computers) can support collocated interaction in groups by providing individuals with private workspaces that can be used alongside shared interaction surfaces. However, such a configuration necessitates the inclusion of intuitive and seamless interactions to move digital objects between displays. While existing research has suggested numerous methods to bridge devices in this manner, these methods often require highly specialized equipment and are seldom examined using real-world tasks. This thesis investigates the use of two cross-device object transfer methods as adapted for use with commonly-available hardware and applied for use in a realistic task, a familiar tabletop card game. \nA digital tabletop and tablet implementation of the tabletop card game Dominion is developed to support each of the two cross-device object transfer methods (as well as two different turn-taking methods to support user identification). An observational user study is then performed to examine the effect of the transfer methods on groups’ behaviour, examining player preferences and the strategies which players applied to pursue their varied goals within the game. The study reveals that players’ choices and use of the methods is shaped greatly by the way in which each player personally defines the Dominion task, not simply by the objectives outlined in its rulebook. Design considerations for the design of cross-device object transfer methods and lessons-learned for system and experimental design as applied to the gaming domain are also offered.
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 | 0.001 | 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