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Record W2024727793 · doi:10.1177/0042098015571240

The persistence of power despite the changing meaning of homeownership: An age-period-cohort analysis of urban housing tenure in China, 1989–2011

2015· article· en· W2024727793 on OpenAlexaff
Qiang Fu

Bibliographic record

VenueUrban Studies · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicChina's Socioeconomic Reforms and Governance
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHousing tenureDemographic economicsRedistribution (election)Cohort effectChinaEconomicsMeaning (existential)Social stratificationLabour economicsEconomic growthPolitical scienceSociologyDemographyPopulationPsychology

Abstract

fetched live from OpenAlex

Using nine successive waves of the China Health and Nutrition Survey (CHNS) data set, this study employs hierarchical age-period-cohort logistic models (HAPC) to analyse temporal patterns of urban homeownership from 1989 to 2011. With the changing meaning of homeownership due to housing reforms, the strong period increases in homeownership track policy changes and the most dramatic increase occurs mainly in the era of housing privatisation rather than housing commodification. The temporal analyses also offer insights into housing stratification from redistribution to markets. The positive effect of education on homeownership is explained by period increases in homeownership, whereas working in state sectors has persistently attached to preferred housing-tenure choice before and after the housing reforms. Moreover, the significant cohort effect lends support to strengthened temporal inequalities in the reform era. These findings not only provide a dynamic understanding of housing stratification in (post)socialist societies, but call for the need to incorporate temporal dimensions into urban studies, especially those on a society experiencing rapid social and institutional changes.

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)0.002
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.740

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.045
GPT teacher head0.292
Teacher spread0.247 · 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 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

Citations37
Published2015
Admission routes1
Has abstractyes

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