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Record W3124543786 · doi:10.1177/0042098009339432

City Size Distribution in China: Are Large Cities Dominant?

2009· article· en· W3124543786 on OpenAlexaff
Zelai Xu, Nong Zhu

Bibliographic record

VenueUrban Studies · 2009
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional Economics and Spatial Analysis
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsChinaEconomic geographyGeographyDistribution (mathematics)Dominance (genetics)Chinese cityPopulation sizeUrbanizationEstimationPopulationEconomic growthDemographyEconomicsBiologySociologyMathematics

Abstract

fetched live from OpenAlex

This paper examines the evolution of the size distribution of Chinese cities. Since the relaxation of restrictions on rural—urban migration in the 1980s, China has experienced rapid urban growth. However, cities of different sizes have experienced varying patterns of growth. First, the evolution of city size distribution in China is described by documenting the growth in city size and in the number of existing cities. Then, focusing on the period from 1990 to 2000, the urban evolutionary trend is analysed by means of the Pareto law estimation and the mobility of cities between different size groups is examined with the Markov transition matrix. The convergence hypothesis in the city population growth process is also tested. The results suggest that, contrary to the expected dominance of large city growth, the Chinese city size distribution evened out during the 1990s, with small cities growing more rapidly than large 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 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.148

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.029
GPT teacher head0.232
Teacher spread0.202 · 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 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

Citations70
Published2009
Admission routes1
Has abstractyes

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