The fat thumb
Bibliographic record
Abstract
Modern mobile devices allow a rich set of multi-finger interactions that combine modes into a single fluid act, for example, one finger for panning blending into a two-finger pinch gesture for zooming. Such gestures require the use of both hands: one holding the device while the other is interacting. While on the go, however, only one hand may be available to both hold the device and interact with it. This mostly limits interaction to a single-touch (i.e., the thumb), forcing users to switch between input modes explicitly. In this paper, we contribute the Fat Thumb interaction technique, which uses the thumb's contact size as a form of simulated pressure. This adds a degree of freedom, which can be used, for example, to integrate panning and zooming into a single interaction. Contact size determines the mode (i.e., panning with a small size, zooming with a large one), while thumb movement performs the selected mode. We discuss nuances of the Fat Thumb based on the thumb's limited operational range and motor skills when that hand holds the device. We compared Fat Thumb to three alternative techniques, where people had to precisely pan and zoom to a predefined region on a map and found that the Fat Thumb technique compared well to existing 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".