Improving Legibility of User Interfaces for Low Vision Conditions with a Crowdsource Platform
Bibliographic record
Abstract
The growing importance of inclusive design solutions has prompted this study \nexamining typography legibility and its impact on accessibility for users with low vision conditions. Focusing on factors such as typographic form, letter spacing, and font size, this research seeks to understand the unique demands of low vision individuals and how typography and user interface design can be adapted to improve legibility and accessibility. Previous research has provided insights into various aspects of typography legibility, but a comprehensive approach addressing the specific needs of low vision users has been lacking. This study contributes to the existing body of knowledge by deconstructing user interfaces (UI) and analyze the fundamental elements affecting legibility. By examining various UI elements and their relationship to text, this research offers personalized, integrated solutions for individuals to tailor websites to their unique needs. \nThe proposed platform differentiates itself from existing accessibility overlays (additional software that is intended to detect and address web accessibility issues on web sites) by emphasizing personalization based on individual preferences, leveraging crowdsourcing to create a variety of modification options. Although the proposal's primary focus is on low vision, it has the potential to assist a wide range of users with various needs. Despite some limitations and challenges faced during the project, this study provides insights into the factors contributing to the legibility of various typefaces, emphasizing the importance of customization to cater to specific needs. Future research should continue to explore these factors, further promoting a more inclusive approach to typography in diverse UI contexts.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".