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Record W2024945634 · doi:10.1177/1078087412463537

Restructuring Japan’s Rustbelt

2012· article· en· W2024945634 on OpenAlexaff
David W. Edgington

Bibliographic record

VenueUrban Affairs Review · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRestructuringNeoliberalism (international relations)CorporationHeavy industryPoliticsLocal governmentEconomyWorkforceGovernment (linguistics)RhetoricEconomic growthPopulationPolitical sciencePolitical economyPublic administrationEconomicsMarket economySociology

Abstract

fetched live from OpenAlex

The crisis of Japan’s political economy raises the question of how it has dealt with the restructuring of its peripheral industrial regions, and the degree to which it has embraced neoliberal policies. I argue that the spread of neoliberalism in Japan has been uneven, shaped by local settings and adopted only selectively. To make this case, the article focuses on restructuring in Muroran City (2010 population, 94,600), a city of steel and heavy industry that lies on the southern coast of Hokkaido. I examine the changing fortunes of Muroran over the 1985-2010 period based upon a number of site visits made in the last 25 years or so. Set against contemporary industrial restructuring in Japan, the article evaluates the actions of the city’s major employer (Nippon Steel Corporation), central government ministries, and programs of the City of Muroran. The results show that despite the rise of the neoliberal rhetoric in Japan, restructuring in Muroran reflected a commitment to manufacturing and the local workforce by corporate as well as central and local governments.

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)0.001
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 score0.035
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
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 head0.036
GPT teacher head0.232
Teacher spread0.196 · 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

Citations11
Published2012
Admission routes1
Has abstractyes

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