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Record W2896470997 · doi:10.1177/0042098018790724

Residential relocation and the remaking of socialist workers through state-facilitated urban redevelopment in Chengdu, China

2018· article· en· W2896470997 on OpenAlexaff
Qinran Yang, David Ley

Bibliographic record

VenueUrban Studies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicChina's Socioeconomic Reforms and Governance
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRedevelopmentRelocationChinaEconomic growthState (computer science)BusinessPolitical scienceEconomics

Abstract

fetched live from OpenAlex

This article discusses the unevenness in the social effects of state-facilitated urban redevelopment in China by examining the social transformation experienced by the housing class of socialist workers in two inner-city redevelopment projects in Chengdu. After government compensation schemes, former public tenants and subsidised owners associated with socialist work-units are far more privileged through cash compensation or relocation in new self-owned apartments than two other housing classes – migrant tenants and homeowners of commodity or rural housing – impacted by the same urban redevelopment. The objective and subjective transformation of socialist workers during the process of resettlement are examined from field interviews, with their status changing from welfare recipients in danwei compounds to proprietors in new gated communities. We conclude that state-facilitated urban redevelopment in the Chinese city is interdependent with, and mutually reinforced by, state-led working-class transformation in market society, so as to balance the two critical national objectives of economic growth and social stability. State dominance in conferring variable opportunities via launching unequal housing trajectories among social groups determines the significant disparity of impacts from urban redevelopment in China.

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)0.001
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 score0.694
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
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 head0.028
GPT teacher head0.325
Teacher spread0.298 · 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

Citations28
Published2018
Admission routes1
Has abstractyes

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