MétaCan
Menu
Back to cohort
Record W2144126006 · doi:10.5430/afr.v2n4p69

The Influence of Financial Industry Cluster on Economic Growth: Three Economic Zones in China

2013· article· en· W2144126006 on OpenAlexvenueno aff
Xiaoyan Zhang, Dafei Chen

Bibliographic record

VenueAccounting and Finance Research · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional Economics and Spatial Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsChinaYangtze riverBeijingCluster (spacecraft)BusinessExclusive economic zoneBusiness clusterEconomicsFinanceEconomic geographyGeographyPolitical science

Abstract

fetched live from OpenAlex

In recent years, financial industry cluster has flourished, promoting economic growth in local and surrounding areas. Yangtze River Delta, Pearl River Delta and Bohai Economic Rim have facilitated economic growth in China. This paper first studies the situation of financial development and financial industry cluster in three economic zones, and finds that Beijing, Shanghai and Shenzhen centralize plenty of financial institutions and financial resources in three economic zones. Then this paper takes L-S model as theoretical basis, verifies the growth effect and radiation effect of financial industry cluster on regional economic growth, and finally puts forwards several pieces of suggestions.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.387
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.261
Teacher spread0.232 · 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
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueAccounting and Finance ResearchSame topicRegional Economics and Spatial AnalysisFrench-language works237,207