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Record W7082678340

Rethinking China’s danwei: lessons from the UK’s housing ‘crisis’

2025· article· en· W7082678340 on OpenAlexaboutno aff

Bibliographic record

VenueUCL Discovery (University College London) · 2025
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsPublic housingBeijingChinaImmigrationHousing tenurePublic policy
DOInot available

Abstract

fetched live from OpenAlex

Under 45 years of neoliberal housing policy, the populations of the world have experienced an appreciably increased housing crisis. This is, first and foremost, a crisis of affordability but it is also a crisis of access, of supply and of quality. The fact these housing crises are occurring in Hong Kong as much as in London, in Vancouver as well as in Beijing – cities that have radically different urban topographies, economies and housing policies – strongly suggests that the usual excuses of insufficient housing densities, lack of access to land, an excess of regulation or an influx of immigrants cannot explain what is a global phenomenon, in both cause and effect. Comparing the regeneration of the UK’s council housing estates with that of a uniquely Chinese type of public housing called the danwei dayuan (work-unit compound), this article outlines some of the key similarities and differences, both economic and social, between policies and practices of public housing and renewal in the UK and China over the last 45 years. Informed by the research and design proposals of Architects for Social Housing CIC, which has pursued research into housing practices and policies for over a decade, this article will propose opportunities for alternative regeneration strategies derived from the respective conditions in the UK and China. To do so, it explores what lessons can be learned from this comparison with a view to increasing the provision, improvement and maintenance of public housing estates and their communities, and – by default – the liveability of our cities.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.503
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0020.002
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 head0.013
GPT teacher head0.207
Teacher spread0.194 · 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 designTheoretical or conceptual
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

Citations1
Published2025
Admission routes1
Has abstractyes

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Same venueUCL Discovery (University College London)Same topicGeochemistry and Geologic MappingFrench-language works237,207