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Record W4391845688 · doi:10.1080/09654313.2024.2314692

Subsidiary networks, connectivity, and urban-regional economic development

2024· article· en· W4391845688 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEuropean Planning Studies · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional Economics and Spatial Analysis
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsEconomic geographyScale (ratio)Production (economics)Space (punctuation)Regional scienceArchitectureBusinessRegional developmentEconomic growthEconomicsGeography

Abstract

fetched live from OpenAlex

This paper argues that urban-regional income development depends on a larger fabric of economic relations at the national and international levels. Focusing on Core-Based Statistical Areas (CBSAs) in the US, the paper identifies firms’ subsidiary networks across space and their changes over time. These networks form a basic architecture through which important growth impulses in production and innovation are transmitted that impact urban income levels. Using a balanced panel of U.S. CBSAs with LexisNexis Corporate Affiliations data from 1993 until 2017, we develop a model that examines the relationship between national and international connectivity and urban income levels, differentiated by origin/destination of ties, industrial sectors, and various interaction effects. Our results strongly support that linkages at both the national and international scale (particularly linkages with European locations) are significantly related to urban-regional income development.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.625
Threshold uncertainty score0.849

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)0.0000.001

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 head0.072
GPT teacher head0.255
Teacher spread0.183 · 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