Are all streets created equal? Measuring the differences in the built environment among streets with various socioeconomic characteristics in Montréal, Canada
Bibliographic record
Abstract
Streets play an important role in shaping urban landscapes and sustaining city life. Through streetscape design, cities can foster vibrant and inclusive neighborhoods that cater to the diverse needs of their residents. Our research aims to determine whether variations at the microscale level of the built environment exist among streets of similar typologies across diverse socioeconomic neighborhoods in Montréal, QC, Canada. The short version of the Microscale Audit of Pedestrian Streetscapes (MAPS-Mini) tool was used to assess microscale features essential for creating high-quality built environments. Assessments were conducted using Google Street View and in-person site visits to ensure a comprehensive analysis of the tool’s effectiveness across different methodologies and urban contexts. Results show significant disparities in the quality of the built environment across various socioeconomic neighborhoods. Despite having identical typologies and characteristics, streets in lower-income areas generally exhibit poorer built environment quality, highlighting that streets are not always created equal in Montréal. This trend is particularly evident in medium and high-density neighborhoods. Less than a third of the audited streets were deemed to have high-quality built environments. This paper can be of value to practitioners working towards addressing disparities in the built environment to create equitable, healthy, and livable communities.
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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 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".