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Geography and Location

2004· book-chapter· en· W4388063274 on OpenAlexaboutno aff
Luis A Rivera-Batiz, Maria-A Oliva

Bibliographic record

VenueInternational Trade · 2004
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional Economics and Spatial Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsMetropolitan areaGeographyPer capitaPopulationEconomic geographyPer capita incomeEconomyArchaeologyEconomicsDemographySociology

Abstract

fetched live from OpenAlex

Abstract The ample evidence of concentration of economic activity presents unmistakable geographical imprints. The shift from rural to industrial and subsequently service economies is mirrored by the development of large metropolitan areas and cities that attract a large proportion of countries ‘ population. London, Paris, New York, Chicago, Los Angeles, Barcelona, Buenos Aires, and other city clusters developed in locations providing access to trade routes and transportation through harbors, rivers, and channels. Canadian manufacturing is heavily concentrated in Ontario. In fact, most of Canada ‘s population and economic activity concentrates near the US border. The US concentration magnets include the East Coast, the West Coast, and the Great Lakes in the Midwest. The Ruhr region is Germany ‘s industrial backbone. The concentration of economic activity and income per capita in ‘northern ‘ countries stands in contrast with ‘southern ‘ countries development plights.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.876
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.026
GPT teacher head0.202
Teacher spread0.176 · 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.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations0
Published2004
Admission routes1
Has abstractyes

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