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Record W2069402926 · doi:10.1068/a3948

Urban Form, Everyday Life, and Ideology: Support for Privatization in Three Toronto Neighbourhoods

2008· article· en· W2069402926 on OpenAlexaffabout
R. Alan Walks

Bibliographic record

VenueEnvironment and Planning A Economy and Space · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNeoliberalism (international relations)IdeologyOpposition (politics)PoliticsWelfare statePerceptionEveryday lifeGovernment (linguistics)SociologyUrban studiesEconomic growthPolitical scienceEconomic geographyPolitical economyGeographyEconomicsLawPsychology

Abstract

fetched live from OpenAlex

One of the trends marking neoliberalism and the attack on the welfare state from the right is the move toward the privatization of public services. Recent research in both the United States and Canada suggests that residents of the suburbs of large urban regions are more likely to vote for political parties on the right and to support neoliberal policies such as privatization, while the opposite is true for inner-city dwellers. However, the reasons why such a spatial division should occur have received little academic attention. This paper seeks to fill this gap in the literature by analyzing the relationship between residential location, spatial factors, and attitudes toward privatization, using survey data collected in the Toronto region. Results suggest that the way urban space influences residents' daily routines and personal experiences may then mediate their perception of the uses of public services and the efficacy of government spending, factors which are found to affect spatial disparities in support of and/or in opposition to privatization. Thus, there is some evidence that urban spatial form is important for understanding the geographic unevenness of support for neoliberalism, and thus ultimately for the production of ideology.

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 score0.345
Threshold uncertainty score0.418

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 head0.018
GPT teacher head0.231
Teacher spread0.213 · 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

Citations30
Published2008
Admission routes2
Has abstractyes

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