Bibliographic record
Abstract
Recently, there has been renewed interest in techniques for facilitating the selection of user interface widgets or other on-screen targets with a pointing device. We report research into using target expansion for facilitating selection. Widgets that expand or grow in response to the user's focus of attention allow for a reduced initial size which can help optimize screen space use and may be easier to select than targets that do not expand. However, selection performance could plausibly suffer from a decreased initial widget size. We describe an experiment in which users select a single, isolated target button that expands just before it is selected. Our results show that users benefit from target expansion even if the target only begins expanding after 90% of the distance to the target has been travelled. Furthermore, our results suggest that, for sufficiently large ID values, users are able to take approximately full advantage of the expanded target size. For interfaces with multiple expanding widgets, however, subtle problems arise due to the collisions or overlap that may occur between adjacent expanding widgets. We give a detailed examination of the issues involved in both untiled and tiled multiple expanding targets and present various design strategies for improving their 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 | 0.001 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".