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Record W2905071171 · doi:10.5539/ijef.v11n1p8

Spatial Correlation of Regional Economic Growth and Regional Coordinated Development Research -----An Empirical Study of Sichuan Province in China

2018· article· en· W2905071171 on OpenAlexvenueno aff
Fengrong Liu, Zhengxia Tang, Lapo Hou

Bibliographic record

VenueInternational Journal of Economics and Finance · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicComplex Network Analysis Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsChinaEconomic geographyGeographyRegional scienceEmpirical researchPositive correlationEconomic growthEconomics

Abstract

fetched live from OpenAlex

Regional economic growth is spatially correlated. With regard to spatial correlation mechanism and characteristics, Network Analysis Method is applied in this paper to discuss the interactions and paths of regional economic development. An empirical study is thus carried out on 18 cities and 3 autonomous prefectures of Sichuan Province to measure their economic network connections and features. We draw main conclusions from this study: (1) the economic network of Sichuan Province shows a typical Core-Periphery structure with Chengdu, the capital city, in the center; (2) geographic location as well as factors such as industrial distributions impose impacts on the formation of the network structure. Finally, based on qualitative evidence and theory discussion, we come up with several suggestions to the coordinated economic development of Sichuan Province.

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)0.000
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 score0.069
Threshold uncertainty score0.357

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.037
GPT teacher head0.333
Teacher spread0.296 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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