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Record W3024641748 · doi:10.1080/00472336.2020.1758955

Legacies of the Cold War in East and Southeast Asia: An Introduction

2020· article· en· W3024641748 on OpenAlexaff
Eva Hansson, Kevin Hewison, Jim Glassman

Bibliographic record

VenueJournal of Contemporary Asia · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSocioeconomic Development in Asia
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCold warAuthoritarianismCapitalismPolitical sciencePoliticsEast AsiaPolitical economyCommunismSoutheast asiaEconomic historyDevelopment economicsHistorySociologyChinaDemocracyLawAncient historyEconomics

Abstract

fetched live from OpenAlex

This article introduces the pieces collected in this special issue on the legacies of the Cold War in East and Southeast Asia. Linking to the Journal of Contemporary Asia’s 50th Anniversary volume, it examines the origins and conflicts associated with the Cold War in Asia. In this special issue, the authors collectively examine the enduring legacies for the region of US engagements that established a set of politically authoritarian regimes trumpeting anti-communism while promoting American-style capitalism. While the path-dependence of this historical moment has not by itself bequeathed Asia’s current crop of authoritarian governments, the authors argue that the current situation cannot be fully understood without reference to Cold War legacies. This introductory article contextualises the pieces in the special issue by providing a broad overview of the variety of Cold War political and economic legacies for the region. It concludes by noting the importance of the kinds of detailed, critical, theoretically informed and empirically rich research that Journal of Contemporary Asia has encouraged since its inception.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0040.005
Scholarly communication0.0070.006
Open science0.0010.003
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0080.001

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 head0.042
GPT teacher head0.268
Teacher spread0.226 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations21
Published2020
Admission routes1
Has abstractyes

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