Superflick: a natural and efficient technique for long-distance object placement on digital tables
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.
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.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".