On the effects of viewing cues in comprehending distortions
Bibliographic record
Abstract
As a community, human-computer information and interface designers have tended to avoid use of fisheyes, and multi-scale presentations with their attendant distortion because of concern about how this distortion may lead to confusion and misinterpretation. On the other hand, for centuries, hand-created information presentations have made regular use of distortion to provide emphasis and actually enhance readability. Is the lack of use in computer presentations because thus far in our computational uses of distortion we have failed to provide adequate support that allows people to comprehend the manner in which the information is being presented? We describe a study about relative difficulty in reading distortions that investigates the effect of the use viewing cues such as the cartographic grid and shading on people's ability to interpret distortions. We look at two interpretation issues: whether people can locate the region of magnification and whether people can read the relative degree of magnification of these regions. We present the findings of this study and a discussion of its results.
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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 | 0.006 | 0.177 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".