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Record W2147470915 · doi:10.1093/ssjj/jym042

How Godzilla Ate Pittsburgh: The Long Rise of the Japanese Iron and Steel Industry, 1900 1973

2007· article· en· W2147470915 on OpenAlex
Bernard Elbaum

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.

Bibliographic record

VenueSocial Science Japan Journal · 2007
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicItaly: Economic History and Contemporary Issues
Canadian institutionsInnovation, Science and Economic Development Canada
Fundersnot available
KeywordsUnderdevelopmentBureaucracyIndustrial policyContext (archaeology)PoliticsPublic policyEconomicsEconomic policyBusinessEconomyMarket economyEconomic growthPolitical scienceGeography

Abstract

fetched live from OpenAlex

From the 1890s to as late as 1960, industrial policy provided vital aid to the development of the Japanese iron and steel industry. Japanese industrial policy proved successful in steel even though public support was much prolonged, subject to political influence, and based on limited forecasting power ex ante, particularly with regard to recurrent raw material problems. Policy success in steel within different time periods suggests that specific targeting mechanisms were less important than the prevalence of market failures within a context of underdevelopment, broad support for industry, and dedicated and capable governmental bureaucracy. By implication, industrial policy in recent years faced greater difficulties insofar as it attempted narrower targeting and operated in a more mature economy.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.027
GPT teacher head0.228
Teacher spread0.201 · 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