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Record W4417147076 · doi:10.1177/23996544251404257

Participatory drainage: Infrastructural citizenship in Montreal’s back alleys

2025· article· en· W4417147076 on OpenAlexafffundabout
Carvajal Sánchez, Sophie L. Van Neste, Kregg Hetherington, Alice Bonneau

Bibliographic record

VenueEnvironment and Planning C Politics and Space · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité de MontréalConcordia University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsGrassrootsCitizenshipBureaucracyCitizen journalismActive citizenshipCorporate governanceParticipatory action researchPower (physics)Ethnography

Abstract

fetched live from OpenAlex

This article examines citizen participation in green infrastructure initiatives in two distinct back-alley drainage projects in Montreal. It studies how planners and city officials attempt to foster “citizen participation” in climate change adaptation, but often inadvertently stifle it through bureaucratic processes and narrow definitions of participation. Drawing on the concept of infrastructural citizenship, the study reveals a disconnect between official expectations and residents’ everyday practices, whose informal contributions and local knowledge are undervalued within formal project frameworks. The analysis highlights how participatory urban projects can depoliticize citizenship and conceal grassroots practices, ultimately undermining project success. By mobilizing critical infrastructure studies, the article sheds light on the power dynamics and governance challenges inherent in participatory drainage, offering insights into the complexities of citizen involvement in urban 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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.401

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.017
GPT teacher head0.266
Teacher spread0.249 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes3
Has abstractyes

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