Gradual Engagement between Digital Devices as a Function of Proximity: From Awareness to Progressive Reveal to Information Transfer
Bibliographic record
Abstract
Connecting and information transfer between the increasing number of personal and shared digital devices in our environment – phones, tablets, and large surfaces – is tedious. One has to know which devices can communicate, what information they contain, and how information can be exchanged. Inspired by Proxemic Interactions, we introduce novel interaction techniques that allow people to naturally connect to and perform cross-device operations. Our techniques are based on the notion of gradual engagement between a person’s handheld device and the other devices surrounding them as a function of finegrained measures of proximity. They all provide awareness of device presence and connectivity, progressive reveal of available digital content, and interaction methods for transferring digital content between devices from a distance and from close proximity. They also illustrate how gradual engagement may differ when the other device seen is personal (such as a handheld) vs. semi-public (such as a large display). We illustrate our techniques within two applications that enable gradual engagement leading up to information exchange between digital devices.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".