Bibliographic record
Abstract
In 2001, 3.6 million Canadians, or nearly 13 percent of the country’s population of over 31 million, lived with a disability. By 2011 this number is expected to grow to 5 million. In response to current demand for accessible transportation and forecast growth, Transport Canada, the federal ministry of transportation, designed the Access to Travel (ATT) Web site. The site is aimed at persons with disabilities, their caregivers, and seniors, with the goal of making travel easier. The site contains information on accessible transportation providers, including their contact information, services and restrictions, policies and procedures, and mechanisms for making inquiries and complaints and providing feedback. The site interfaces with technological devices used by persons with disabilities, including screen readers and voice recognition software, and is capable of switching from regular to large text size for low-vision users. This document explores the process used to develop the site, from initial research to the technical and policy challenges the department faced in developing the ATT Web site, and the solutions that led to implementation. Two models are described, one in which the federal government is the major partner, and one in which a public-private partnership approach is taken to deliver an accessible transportation Web site to the target audience. The paper includes best practices for the design and development of accessible Web site design; the collection and dissemination of accessible transportation information; and successful management of collaborative partnerships with contributing partners and stakeholders. The paper closes with an evaluation of the ATT Web site.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.336 | 0.152 |
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".