Making Montreal’s Indoor City Accessible for People With Disabilities
Bibliographic record
Abstract
Indoor pedestrian networks are a facet of the built environment in many cities around the world.They can be built for many reasons, including separating pedestrians from motor vehicle traffic, providing a refuge from seasonal inclement weather, or monetizing otherwise unused floors of office buildings.In Montreal, an indoor city has been in existence since 1962 and has grown to a length of 32 km the downtown area.While previous studies have examined the network growth and its effects on the levels of accessibility to retail space within the indoor city, the results of these studies do not hold true for people with disabilities.This research examines the ability of a person with physical disabilities and/or mobility impairments to function within Montreal's indoor city.This is done through examining the existing indoor network and measuring the existing barriers that a person with disabilities faces when moving inside Montreal indoor city using a simple accessibility measure.Also in this research we develop several scenarios to determine the most important links that can substantially increase the accessibility levels for the people with physical disabilities.Results suggest that while certain segments are more accessible than others, the majority of the Indoor City is currently inaccessible to people with disabilities.The paper ends with a set of recommendations for upgrading key connection points to increase the level of accessibility inside the Indoor City; legislative improvements aimed at ensuring accessibility in future extensions and as part of any major renovations; organizational improvements, such as a dedicated Indoor City municipal department; and the launch of a RÉSO website.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.029 | 0.003 |
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".