Bibliographic record
Abstract
Interactive fisheye views allow users to edit data and manipulate objects through the distortion lens. Although several varieties of fisheye lens exist, little is known about how the different types fare for different interactive tasks. In this paper, we investigate one kind of interaction – layout of graphical objects – that can be problematic in fisheyes. Layout involves judgments of distance, alignment, and angle, all of which can be adversely affected by the distortion of a fisheye. We compared performance on layout tasks with three kinds of fisheye: a full-screen pyramid lens, a constrained hemispherical lens, and a constrained flat-topped hemisphere. We found that accuracy was significantly better with the constrained lenses compared to the full-screen lens, and also that the simple hemisphere was better at higher levels of distortion than the flat-topped version. The study shows that although there is a cost to doing layout through distortion, it is feasible, particularly with constrained lenses. In addition, our findings provide initial empirical evidence of the differences between competing fisheye varieties.
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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 | 0.003 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".