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Record W1973891179 · doi:10.1177/0891242412453481

Collaborative Regionalism and Foreign Direct Investment

2012· article· en· W1973891179 on OpenAlexaboutno aff
A. J. Jacobs

Bibliographic record

VenueEconomic Development Quarterly · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Urban Networks and Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsAllianceForeign direct investmentGeneral partnershipRegionalism (politics)Political scienceAutomotive industryInvestment (military)International tradeEconomyBusinessEconomic growthEconomicsEngineeringFinancePoliticsDemocracy

Abstract

fetched live from OpenAlex

Jurisdictions in the Southeast Automotive Core (SEAC), encompassing Alabama, Georgia, Mississippi, South Carolina, and Tennessee, have attracted 8 of the 11 light vehicles assembly plants built by the “New Domestics” in the United States over the past 20 years (i.e., Toyota, Honda, Nissan, Hyundai, Volkswagen, Mercedes, and BMW). Through case studies of the Toyota-PUL Alliance of Northeast Mississippi and the Hyundai-Kia Auto Valley Partnership of east-central Alabama and west-central Georgia, this article chronicles how by working together, certain subregions within the SEAC have gained a comparative advantage in their competitions for New Domestics Foreign Direct Investment. Overall, the findings of this study show how local governments in the form of the collaborative region still can be an important economic development agent within an ever-globalizing economy. As a result, this article should prove informative to development scholars and practitioners in the United States and Canada, especially in areas combating economic/fiscal distress.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.005
Scholarly communication0.0040.002
Open science0.0000.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.011
GPT teacher head0.242
Teacher spread0.231 · 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

Citations14
Published2012
Admission routes1
Has abstractyes

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