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Record W3215320189 · doi:10.1080/02673037.2021.2004093

Neoliberalization and inequality: disparities in access to affordable housing in urban Canada 1981–2016

2021· article· en· W3215320189 on OpenAlexafffundabout
Yushu Zhu, Yue Yuan, Jiaxin Gu, Qiang Fu

Bibliographic record

VenueHousing Studies · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsUniversity of British ColumbiaSimon Fraser University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsAffordable housingFinancializationMetropolitan areaCensusInequalityImmigrationDemographic economicsVulnerability (computing)EconomicsEconomic growthGeographySociologyFinancePopulationDemography

Abstract

fetched live from OpenAlex

The neoliberalization of housing policy and housing financialization have brought unequal impacts on housing outcomes. Drawing on eight waves of census data, this study uncovers the changing mechanism of housing stratification in selected Canadian census metropolitan areas from 1981 to 2016, a period when Canada transitioned from a welfare housing regime to a neoliberal regime. This study reveals entrenched housing inequality and strengthened income effect in determining access to affordable housing in the neoliberal era. Housing financialization has significantly contributed to intensified inequality in accessing affordable housing. Access to affordable housing in Canada is also stratified along the lines of gender and immigration status. Homeownership affordability for low-to-moderate-income households has significantly deteriorated over time, representing a new form of housing vulnerability in the neoliberal era.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.060
Threshold uncertainty score0.432

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.006
Science and technology studies0.0040.002
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.271
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 source (direct Gemma or distilled Codex), 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

Citations55
Published2021
Admission routes3
Has abstractyes

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