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Record W1972816077 · doi:10.1680/muen.2007.160.2.71

Community design with children in Montreal and Guadalajara

2007· article· en· W1972816077 on OpenAlexaffabout
Juan Torres, Marie Lessard

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Municipal Engineer · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Environments and Student Outcomes
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsNeighbourhood (mathematics)Citizen journalismCommunity designArchitectureLandscape architectureParticipatory designUrban designUrban planningParticipatory planningProcess (computing)SociologyEnvironmental planningGeographyPolitical scienceEngineeringCivil engineeringComputer scienceOperations management

Abstract

fetched live from OpenAlex

This paper examines two community design projects that were carried out in 2004–2006 in Montreal, Canada and Guadalajara, Mexico as part of UNESCO's Growing Up in Cities programme. In both cases, children aged 8 to 16, in collaboration with students in architecture, landscape architecture and urban planning, evaluated their neighbourhoods and proposed physical transformations to improve outdoor spaces. The collaborative process made it possible to understand how children perceive and use their neighbourhoods, to increase children's environmental knowledge and skills, and to introduce students to participatory design. This paper discusses this participatory process, describes the characteristics of places that children perceive to be meaningful and determines what lessons in neighbourhood planning can be drawn from these experiences.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.443
Threshold uncertainty score0.891

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0110.005
Scholarly communication0.0030.001
Open science0.0020.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.018
GPT teacher head0.265
Teacher spread0.246 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations2
Published2007
Admission routes2
Has abstractyes

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