Superflick: a natural and efficient technique for long-distance object placement on digital tables
Why this work is in the frame
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Bibliographic record
Abstract
Moving objects past arms' reach is a common action in both real-world and digital tabletops. In the real world, the most common way to accomplish this task is by throwing or sliding the object across the table. Sliding is natural, easy to do, and fast: however, in digital tabletops, few existing techniques for long-distance movement bear any resemblance to these real-world motions. We have designed and evaluated two tabletop interaction techniques that closely mimic the action of sliding an object across the table. Flick is an open-loop technique that is extremely fast. Superflick is based on Flick, but adds a correction step to improve accuracy for small targets. We carried out two user studies to compare these techniques to a fast and accurate proxy-based technique, the radar view. In the first study, we found that Flick is significantly faster than the radar for large targets, but is inaccurate for small targets. In the second study, we found no differences between Superflick and radar for either time or accuracy. Given the simplicity and learnability of flicking, our results suggest that throwing-based techniques have promise for improving the usability of digital tables.
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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 | 0.000 |
| 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