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Record W2143055265 · doi:10.1093/jeg/4.2.107

The rise (and decline) of American regional science: lessons for the new economic geography?

2004· article· en· W2143055265 on OpenAlexafffund
Trevor J. Barnes

Bibliographic record

VenueJournal of Economic Geography · 2004
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional Economics and Spatial Analysis
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of CanadaLondon School of Economics and Political Science
KeywordsContext (archaeology)Regional geographyEconomic scienceHistory of scienceWork (physics)Economic geographySociologyHistorical geographyHuman geographyRegional scienceSocial scienceGeographyEpistemologyArchaeology

Abstract

fetched live from OpenAlex

Abstract Regional science weaves in and out of the story of post-war economic geography. The vision of one man, the American economist Walter Isard, regional science represented the first systematic attempt to further joint work between geographers and economists. Within this context, the tasks of the paper are twofold. The first is to provide an interpretative history of the rise of regional science, and to a much lesser extent its decline. The interpretative framework derives from science studies, and in particular the work of Bruno Latour. The history is based on archival material and interviews. The second is to speculate briefly on the implications of both the interpretive framework used in the paper, and the history of regional science told, for the new economic geography that similarly attempts to convene discussions between economists and geographers.

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)0.010
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0050.031
Scholarly communication0.0170.017
Open science0.0010.003
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0070.000

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 head0.024
GPT teacher head0.255
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 designTheoretical or conceptual
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

Citations56
Published2004
Admission routes2
Has abstractyes

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