A Comparison of Techniques for In-place Toolbars
Bibliographic record
Abstract
Selections are often carried out using toolbars that are located far away from the location of the cursor. To reduce the time to make these selections, researchers have proposed in-place toolbars such as Toolglasses or popup palettes. Even though in-place toolbars have been known for a long time, there are factors influencing their performance that have not been investigated. To explore the subtleties of different designs for in-place toolbars, we implemented and compared three approaches: warping the cursor to the toolbar, having the toolbar pop up over the cursor, and showing the toolbar on the trackpad itself to allow direct touch. Our study showed that all three new techniques were faster than traditional static toolbars, but also uncovered important differences between the three in-place versions. Participants spent significantly less time in the direct-touch trackpad, and warping the cursor's location caused a time-consuming attentional shift. These results provide a better understanding of how small changes to in-place toolbar techniques can affect performance.
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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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".