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Record W2004039679 · doi:10.1631/jzus.2007.a0164

A model of regional economic development with increasing returns

2006· article· en· W2004039679 on OpenAlexaff
Edward Y. Qian, Yao Yao-jun, Gary Chen

Bibliographic record

VenueJournal of Zhejiang University. Science A · 2006
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional Economics and Spatial Analysis
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsEconomicsReturns to scaleEconometricsCapital (architecture)Scale (ratio)Constant (computer programming)PopulationValue (mathematics)Argument (complex analysis)RentingPath (computing)Mathematical economicsMicroeconomicsMathematicsProduction (economics)GeographyComputer science

Abstract

fetched live from OpenAlex

This paper develops mathematically and empirically tractable regional and interregional model of economic development with increasing returns to scale (IRS) under the neoclassical assumptions. A one-sector, two-region model in which one region exhibits IRS is presented and the whole nation presents constant returns to scale. The development of the local IRS economy is shown to be constrained to a “moving equilibrium” path. The preliminary empirical results are sufficiently supportive of the argument to encourage further research along the lines of the model. In particular, the neoclassical model does not predict negative coefficients on the real rental value of capital in regressions explaining population or employment relative to that in the nation.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0030.004
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.002

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 head0.028
GPT teacher head0.181
Teacher spread0.153 · 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 designSimulation or modeling
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
Published2006
Admission routes1
Has abstractyes

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