Web Accessibility for Older Adults: A Comparative Analysis of Disability Laws
Bibliographic record
Abstract
Access to the Internet is increasingly critical for health information retrieval, access to certain government benefits and services, connectivity to friends and family members, and an array of commercial and social services that directly affect health. Yet older adults, particularly those with disabilities, are at risk of being left behind in this growing age- and disability-based digital divide. The Americans with Disabilities Act (ADA) was designed to guarantee older adults and persons with disabilities equal access to employment, retail, and other places of public accommodation. Yet older Internet users sometimes face challenges when they try to access the Internet because of disabilities associated with age. Current legal interpretations of the ADA, however, do not consider the Internet to be an entity covered by law. In this article, we examine the current state of Internet accessibility protection in the United States through the lens of the ADA, sections 504 and 508 of the Rehabilitation Act, state laws and industry guidelines. We then compare U.S. rules to those of OECD (Organisation for Economic Co-Operation and Development) countries, notably in the European Union, Canada, Japan, Australia, and the Nordic countries. Our policy recommendations follow from our analyses of these laws and guidelines, and we conclude that the biggest challenge in bridging the age- and disability-based digital divide is the need to extend accessibility requirements to private, not just governmental, entities and organizations.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".