The Effect of Visual Appearance on the Performance of Continuous Sliders and Visual Analogue Scales
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Sliders and Visual Analogue Scales (VASs) are input mechanisms which allow users to specify a value within a predefined range. At a minimum, sliders and VASs typically consist of a line with the extreme values labeled. Additional decorations such as labels and tick marks can be added to give information about the gradations along the scale and allow for more precise and repeatable selections. There is a rich history of research about the effect of labelling in discrete scales (i.e., Likert scales), however the effect of decorations on continuous scales has not been rigorously explored. In this paper we perform a 2,000 user, 250,000 trial online experiment to study the effects of slider appearance, and find that decorations along the slider considerably bias the distribution of responses received. Using two separate experimental tasks, the trade-offs between bias, accuracy, and speed-of-use are explored and design recommendations for optimal slider implementations are proposed.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it