Accessibility of graphics in technical documentation for the cognitive and visually impaired
Bibliographic record
Abstract
With the U.S. government's new requirement for accessibility, companies such as IBM, are revising their method of selling products and solutions to ensure compliance. The delivery mechanism for information must be accessible to all users, including users with vision, hearing, motor, or cognitive disabilities.Users consume information from many different sources. An increasingly popular method of distributing information is using computers and the Internet. The Web houses volumes of documents and graphics available to anyone at any time. Paired with assistive technology such as Home Page Reader, the Internet makes information that would otherwise be restrictive accessible.However, as approachable as the Internet may be with its sheer volume of information, it does have limitations. The old saying about a chain, that it is only as good as its weakest link, aptly describes the Internet. Beside problems with retrievability and searchability, many other issues plague this vehicle of information. No matter how sophisticated HTML, DHTML, XHTML, and XML present information, the graphics within the body text are the weakest link, from the viewpoint of users with visual or cognitive impairments.This presentation is divided into two sections and explores how a method of creating and exporting graphics can improve the experiences of users with visual or cognitive impairments when viewing technical documentation:
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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; 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".