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Record W4401063180 · doi:10.1177/0739456x241264265

Collective Efficacy and Mixed-Tenure Redevelopment: Insights from Toronto’s Regent Park Neighborhood

2024· article· en· W4401063180 on OpenAlexafffundabout
Daniel J. Rowe, James R. Dunn

Bibliographic record

VenueJournal of Planning Education and Research · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsSt. Michael's HospitalMcMaster UniversityUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaJohn D. and Catherine T. MacArthur Foundation
KeywordsCollective efficacyRegentRedevelopmentPublic parkPublic housingInterpersonal tiesCollective actionPerceptionSociologySocioeconomicsEconomic growthBusinessGeographyPolitical sciencePsychologyEconomicsEnvironmental planningSocial science

Abstract

fetched live from OpenAlex

It is asserted that mixed-tenure public housing redevelopments can improve informal social control in targeted neighborhoods. We investigate this question using a validated measure of collective efficacy from a survey of residents in Toronto’s Regent Park neighborhood. We find that social housing residents report higher perceived levels of collective efficacy than do residents of the market buildings and that households with children report higher perceptions of collective efficacy than do households without children. Our findings provide some support for existing best practices, particularly the provision of amenities for families and the preservation of social ties among social tenants.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.992
Threshold uncertainty score0.531

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.107
GPT teacher head0.509
Teacher spread0.402 · 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

Citations0
Published2024
Admission routes3
Has abstractyes

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