Beyond user interfaces in mobile accessibility: Not just skin deep
Bibliographic record
Abstract
With the increasing popularity of mobile technologies, users today are able to access information from virtually anywhere. Examples include localization via the Global Positioning System (GPS), Internet access through cellular and WiFi networks, etc. Using assistive technologies, people with disabilities can live more independently than ever before. However, most of the current mobile applications are not developed with accessibility in mind. This paper uses mobile applications for public transit systems as a case study, and presents an extension of our prototype ABLE (Accessible Bussing through Location Estimation) Transit. Based on an estimate of the user's current location, ABLE Transit leverages the location services on mobile devices and public transit information to reveal schedule and route information in accessible formats. We derive four groups of personas that need to be addressed when designing accessible software. While the cross platform accessibility is preserved at the user interface level, we further investigated system level concerns and implemented two data storage strategies for large transit schedule data. We identify the tradeoffs of Web versus native applications, local versus remote data storage when developing assistive technology, and discovered that accessibility at system level is particularly challenging.
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.
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 | 0.000 | 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 | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".