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Record W4405156800 · doi:10.1111/anti.13122

The National Security State and the Tech City: Social Structures of Militarisation in Seattle's Long Cold War

2024· article· en· W4405156800 on OpenAlexafffund
Chris Meulbroek, Jim Glassman

Bibliographic record

VenueAntipode · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicChina's Socioeconomic Reforms and Governance
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of CanadaAmerican Association of Geographers
KeywordsCold warState (computer science)Political scienceNational securityEconomic historyPublic administrationSociologyLawHistoryPolitics

Abstract

fetched live from OpenAlex

Abstract Integration between the American state and technology capital has deep roots in the military‐industrial complex that has structured the US geopolitical economy since World War II. This article makes the case for understanding the urban‐regional dimensions of technology capital in terms of a transformation of, rather than as a departure from, Cold War geopolitics. It introduces the concept of a social structure of militarisation to analyse the transition from military‐Keynesianism to tech‐oriented militarism, developing this concept through a case study of high‐tech firms in the Seattle region. Our analysis shows how the class dynamics that underpinned the growth of Seattle's “high‐tech” aerospace sector in the postwar period conditioned the subsequent growth of the information and biotechnology sectors. Like other tech cities, Seattle's economy remains a privileged site of technical and managerial labour within the extended US national security state.

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)0.000
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 score0.080
Threshold uncertainty score0.158

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0040.005
Scholarly communication0.0030.001
Open science0.0000.003
Research integrity0.0000.001
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 head0.296
Teacher spread0.285 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

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