MétaCan
Menu
Back to cohort

Intersecting perspectives: A participatory street review framework for urban inclusivity

2025· article· en· W4412948921 on OpenAlex
Rashid Mushkani, Shin Alexandre Koseki

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueHabitat International · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicUrban, Neighborhood, and Segregation Studies
Canadian institutionsUniversité de MontréalMila - Quebec Artificial Intelligence Institute
FundersSocial Sciences and Humanities Research CouncilSocial Sciences and Humanities Research Council of CanadaFonds de Recherche du Québec-Société et CultureFonds de recherche du Québec
KeywordsCitizen journalismSociologyEnvironmental planningPolitical scienceGeography

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.942
Threshold uncertainty score0.601

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.061
GPT teacher head0.419
Teacher spread0.357 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it