Inclusion of People With Disabilities in Public Transportation: A Case-Study Analysis of Canada and U.S. Policies
Bibliographic record
Abstract
Public transportation is fundamental for people with disabilities to access meaningful opportunities. However, their access to public transit is still limited, partly due to the policies establishing the accessibility requirements of public transportation services. We conducted a policy scan analysis of the local, provincial/state, and federal public transportation policies in Canada and the United States. With this scan, we analyzed the policies’ aims, the definition of disabilities, and the content of those policies. The policy analysis revealed that the definition of disability was inconsistent across jurisdictions, creating a discrepancy between those who have access to accessible transport and those who do not. The analysis also showed that policies in the United States and Canada mainly focused on the built environment, the adaptations of vehicles, and the provision of services. In contrast, only a few policies covered social accessibility, such as interaction with the staff, which underscores a significant policy gap and suggests the need for more training requirements in the policies. Funding to transport agencies for people with disabilities was also a missing piece highlighted in this comparative analysis, particularly in Canada. Policymakers need to develop funding mechanisms to ensure a better implementation of accessibility in services and infrastructure.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.019 |
| Science and technology studies | 0.019 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".