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Record W3107911972 · doi:10.1177/0042098020964439

The representativeness of neighbourhood associations in Toronto and Vancouver

2020· article· en· W3107911972 on OpenAlexafffundabout
Aaron Alexander Moore, R. Michael McGregor

Bibliographic record

VenueUrban Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsToronto Metropolitan UniversityUniversity of Winnipeg
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsNeighbourhood (mathematics)Representativeness heuristicPoliticsVotingCorporate governanceGeographyPopulationIdeologyDemographic economicsSociologyPolitical scienceDemographySocial psychologyPsychologyEconomics

Abstract

fetched live from OpenAlex

Neighbourhood associations are major players in urban politics throughout North American cities and increasingly are becoming a political force in other parts of the world. However, while there is a rich and well-developed literature on the role played by neighbourhood associations in urban politics, few studies examine whether their membership reflects the socio-demographic composition and interests of the broader public. This paper addresses this gap in the literature using survey data from voters conducted during the Vancouver and Toronto 2018 municipal elections. We compare the responses of participants who identify as members of neighbourhood associations (or their equivalents) with those of the broader voting public. We find that members of neighbourhood associations in both cities are not representative of the broader population. They are more likely to be white, older and have higher education than the average voter. In addition, while the ideology of neighbourhood association members differs little from that of the broader public, their policy priorities are different from those of the majority of voters in both cities. Our findings suggest that neighbourhood associations fail in providing descriptive representation and may not offer substantive representation. These findings raise important questions about the role of neighbourhood associations in local governance. Our study also demonstrates the merit of using individual-level surveys to learn more about the composition and policy preferences of neighbourhood associations.

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)0.001
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 score0.426
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.058
GPT teacher head0.366
Teacher spread0.309 · 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

Citations6
Published2020
Admission routes3
Has abstractyes

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