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Record W1978081090 · doi:10.1191/1474474003eu267oa

Disaster and decentralization: American cities and the Cold War

2003· article· en· W1978081090 on OpenAlexafffund
Matthew Farish

Bibliographic record

VenueCultural Geographies · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicVietnamese History and Culture Studies
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of CanadaKillam Trusts
KeywordsSuburbanizationUrbanismGeopoliticsPolitical sciencePolitical economyDecentralizationCapitalismCold warPoliticsUrbanizationChampionNuclear weaponEconomySociologyEconomic historyHistoryEconomic growthLawArchitecturePopulation

Abstract

fetched live from OpenAlex

The atomic bombing of Hiroshima and Nagasaki also ushered in an era of anxious urbanism in the USA. Despite its status as the inheritor of European modernism, the champion of capitalism and the centre of a rapidly globalizing popular culture, America still struggled with the contradictory results of urbanization and military supremacy. In this essay, I bring political and urban geography together in a study of American cities and their role as strategic environments in the developing geopolitical conflict of the Cold War. New technologies such as the atomic bomb prompted a diverse wave of lurid disaster scenarios, as well as subsequent scientific attempts to contain, control and reduce risk and danger. Whether considered or far-fetched, these schemes were profoundly geographical, and borrowed much from the logic of postwar social science. In subtle yet pervasive ways they contributed to the prominent discourses of urban decline and suburbanization, and thus to the changing material fabric of postwar American cities.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score0.233

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0060.013
Scholarly communication0.0050.002
Open science0.0000.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.011
GPT teacher head0.253
Teacher spread0.242 · 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 designQualitative
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

Citations32
Published2003
Admission routes2
Has abstractyes

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