Universal Design in Legislation: Eliminating Barriers for People with Disabilities
Bibliographic record
Abstract
Anyone who buys consumer electronics knows the frustration of trying to figure out how to use them. This frustration is amplified in the case of consumers with disabilities. Too often they find that consumer products are designed on the palpably incorrect basis that all those who will use them have two working eyes, ears, legs, feet, and hands and can mentally process information in the way most others do. In recent years, a movement known as ‘Universal Design’ has grown in the design of consumer products, as well as in the design of buildings and other facilities. Universal design calls for products and facilities to be designed so that all people can use them, not just persons without disabilities. In many instances, products and buildings incorporating Universal Design end up benefitting all consumers, whether or not they have a disability. When a building's entryway is designed without steps and with an automated door, this contributes to its accessibility for persons with disabilities. It also facilitates access for people with shopping carts, baby strollers, and luggage. When a telephone's call display system is augmented with audible call display (a computerized voice that announces the telephone number of incoming calls), it not only makes call display technology accessible for users with vision impairments but also relieves sighted users of the need to sprint across rooms to look in a tiny display window to determine a caller's identity. The basic premises of Universal Design are (i) that persons with disabilities ought to have meaningful access to the same products, buildings, and facilities as everyone else and (ii) that enhanced accessibility benefits everyone.
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 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 | 0.031 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.008 | 0.024 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".