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Record W3121885991

The Empirics of Agglomeration and Trade

2003· preprint· en· W3121885991 on OpenAlexaff
Keith Head, Thierry Mayer

Bibliographic record

VenueSPIRE (Sciences Po) · 2003
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional Economics and Spatial Analysis
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEconomies of agglomerationRubricEconomic geographyEconomicsNew trade theoryScale (ratio)Economies of scaleEmpirical researchInternational tradeMicroeconomicsTrade barrierGeographySociologyMathematics
DOInot available

Abstract

fetched live from OpenAlex

This chapter examines empirical strategies that have been or could be used to evaluate the importance of agglomeration and trade models. This theoretical approach, widely known as "New Economic Geography" (NEG), emphasizes the interaction between transport costs and firm-level scale economies as a source of agglomeration. NEG focuses on forward and backward trade linkages as causes of observed spatial concentration of economic activity. We survey the existing literature, organizing the papers we discuss under the rubric of five interesting and testable hypotheses that emerge from NEG theory. We conclude the chapter with an overall assessment of the empirical support for NEG and suggest some directions for future research.

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)0.002
metaresearch head score (Gemma)0.012
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0020.014
Scholarly communication0.0060.009
Open science0.0020.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0120.002

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 head0.057
GPT teacher head0.252
Teacher spread0.195 · 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

Citations94
Published2003
Admission routes1
Has abstractyes

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