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Record W3124543786 · doi:10.1177/0042098009339432

City Size Distribution in China: Are Large Cities Dominant?

2009· article· en· W3124543786 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.261
Threshold uncertainty score0.675

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.029
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