Intersecting perspectives: A participatory street review framework for urban inclusivity
Bibliographic record
Abstract
Urban demographic changes, evolving multiculturalism, and heightened tourism flows have underscored the importance of designing public streets that serve heterogeneous populations. Despite municipal policies advocating equity and universal access, many streetscapes still fall short of accommodating the wide-ranging practical and cultural differences that exist among diverse user groups. This paper introduces and applies a participatory methodology—“Street Review”—designed to capture how individuals from varying social positions evaluate an array of streets within a multicultural metropolis. Grounded in the context of Montréal, known for its overlapping layers of historic and modern neighborhoods, multilingual communities, and continual inflows of short-term visitors, this framework draws upon qualitative interviews, focus groups, and a systematic rating of street images by 12 participants. The analyses focus on perceived inclusivity, accessibility, aesthetics, and practicality for both long-term residents (post-occupancy) and newcomers or suburban visitors (pre-occupancy). Findings from examining 20 selected streets (represented through 60 vantage points) indicate that most streetscapes offer moderate levels of user-friendliness, with only a handful of locations scoring especially low on supporting vulnerable populations or signaling cultural welcome. A smaller subset approached higher performance in certain areas but rarely satisfied all participant groups. In situating these results within global debates around inclusive urban design, public space, and the interplay of tourism with social equity, we illustrate how group-based deliberations can generate constructive insights and spotlight deeper conflicts rooted in identity, memory, and everyday mobility. These reflections inform planners and policymakers in striving for streets that address the convergence of diverse user experiences and emerging global challenges in urban policy.
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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 | 0.229 | 0.137 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.025 | 0.066 |
| Scholarly communication | 0.022 | 0.018 |
| Open science | 0.008 | 0.027 |
| Research integrity | 0.006 | 0.007 |
| 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".