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Record W1981944860 · doi:10.1155/2013/958738

Morphology and Spatial Dynamics of Urban Villages in Guangzhou’s CBD

2013· article· en· W1981944860 on OpenAlexaff
John Zacharias, Yue Hu, Quan Le Huang

Bibliographic record

VenueUrban Studies Research · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicChina's Socioeconomic Reforms and Governance
Canadian institutionsConcordia University
Fundersnot available
KeywordsGeographyEconomic geographyRegional scienceScope (computer science)Space (punctuation)Environmental planningEconomyCartographySocioeconomicsSociologyComputer science

Abstract

fetched live from OpenAlex

Studies on the urban village ( chengzhongcun ) over the past ten years have focussed on legalistic and structural aspects, as well as the social outcomes of village-led redevelopment. Studies on the morphology of villages, their spatial and economic linkage with the city, and their internal spatial dynamics are, in comparison, limited in number and scope. This study of village space in the new central area of Guangzhou focusses on the spatial relationships between village space and the surrounding city—the exchange of people and goods, the movement system in relation to commercial activity, and the relationship between the pattern of building and movement networks—as a primer for new approaches to physical renewal. Primary field data, interviews, and archival research support the analysis of Shipai village, in particular. It was found that Shipai plays a significant role in transport and economy at the district and central city level. The internal movement system functions to connect surrounding areas while supporting a commercial and services system of local and district-level significance. The built form of the village is also self-generated by location and internal rule making. The physical and activity patterns of the self-rebuilt village exhibit the characteristics of emergent spontaneous order.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.038
Threshold uncertainty score0.935

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.057
GPT teacher head0.386
Teacher spread0.329 · 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 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

Citations16
Published2013
Admission routes1
Has abstractyes

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