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Record W2288942223

Geographical Concentration of Soviet Industries: A Comparative Analysis

2015· article· en· W2288942223 on OpenAlexaboutno aff
D Kofanov, Tatiana Mikhailova

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Productivity
Canadian institutionsnot available
Fundersnot available
KeywordsSpillover effectExternalityEconomic geographyDispersion (optics)ManufacturingPopulationManufacturing sectorEconomies of agglomerationBusinessInternational tradeEconomicsInternational economicsEconomic growthMacroeconomicsDemographyMicroeconomics
DOInot available

Abstract

fetched live from OpenAlex

This paper analyzes the geographical concentration of manufacturing industries in Russia before the beginning of transition. We calculate Duranton- Overman (Duranton, Overman, 2005) indices of localization and dispersion for 4-digit US SIC 1987 industries of civilian manufacturing in the RSFSR in 1989. Comparative analysis reveals that industries in the RSFSR were less localized than in geographically compact countries of Western Europe. On the other hand, compared to another country with large territory and low population density - Canada, industries in RSFSR exhibit similar overall degree of geographical dispersion. Compared to Canada, Russia has less localization in the technologically sophisticated industries. Such pattern of industrial localization suggests that the Soviet planning system could account for the benefits of localization near inputs or near consumers, but could not internalize the knowledge spillover externalities, that are especially important for the technologically advanced industries.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.148
Threshold uncertainty score0.530

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.106
GPT teacher head0.260
Teacher spread0.154 · 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.

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

Citations6
Published2015
Admission routes1
Has abstractyes

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