Accessibility of third-party transit apps and the role of transit agencies and their open data
Bibliographic record
Abstract
Transit agencies like other municipal and other governments and agencies are increasingly adopting open data agendas, often in the form of policy initiatives. Transit agencies are also subject to legislatively mandated accessibility requirements for their service offerings, for instance in the United States, the Americans with Disabilities Act or Section 508 of the Rehabilitation Act. What happens at the convergence of transit agencies’ open data initiatives and regulatory requirements? In this paper, we examine this question through the lens of transit open data used to develop smartphone apps that are used to navigate the transit agencies’ services. Using a qualitative approach, we investigate the extent of open data usage by third parties, the nature of the relationships between transit agencies and open data users, and, particularly, the extent to which the requirements of disability accessibility compliance are made of open data users by the transit agencies. We find that, despite their inferred relevance, there was no required compliance of accessibility regulations in the open-data products, third-party transit apps, except by one transit agency, highlighting discord at the convergence of open data initiatives and regulatory requirements. The purpose of the study is to create knowledge around the convergence to inform policy making and opportunities for change as transit operators continue to make open data available.
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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 | 0.018 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".