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Record W4388704584 · doi:10.22158/uspa.v6n4p90

Tracing the Grotesque: Finding Value in the Urban Villages of Shenzhen

2023· article· en· W4388704584 on OpenAlexaff
Yunhao Yang

Bibliographic record

VenueUrban Studies and Public Administration · 2023
Typearticle
Languageen
FieldEngineering
TopicUrban Design and Spatial Analysis
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsModernization theoryUrban villageInclusion (mineral)Value (mathematics)GeographyUrban planningPublic spaceSpace (punctuation)SocioeconomicsPublic lifeChinaUrban spaceEconomic growthSociologyEnvironmental planningPolitical scienceSocial scienceCivil engineeringEngineeringArchitectural engineering

Abstract

fetched live from OpenAlex

Commercial housing communities are rapidly developed in the city of Shenzhen since the 1979 economic reform. In contrast, the still remaining village houses are excluded from the urban planning strategy and they are described as the “dirty, unorganized, bad” Urban Villages. The formally constructed Shenzhen metropolis, which was intended to greatly improve life in Shenzhen through modernization, somehow fails to provide full sense of rootedness to the residents, while the residents who live within urban villages are having surprisingly positive connections with these low-end neighborhoods. This article has the ambition to promote inclusion and support the maintenance of the Urban Village type. It also aims to discuss how the design of public space contributes to mental wellbeing of residents; how access to public space allows for social inclusion; and also, do Urban Villages deserve to be mostly preserved and how can these communities co-exist with the whole city.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0040.005
Scholarly communication0.0020.002
Open science0.0010.004
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.043
GPT teacher head0.263
Teacher spread0.220 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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