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Record W7000148281

Entwined Imperial Networks: Reading Cold War Afterlives in Contemporary Asian American and Asian Representations

2023· article· en· W7000148281 on OpenAlexaboutno aff

Bibliographic record

VenueScholarSpace (University of Hawaii at Manoa) · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicAsian American and Pacific Histories
Canadian institutionsnot available
Fundersnot available
KeywordsMilitarizationCold warState (computer science)ColonialismComplicityAsian studiesRelation (database)Decolonization
DOInot available

Abstract

fetched live from OpenAlex

This dissertation examines the afterlives of US Cold War military interventions in post-WWII North America and Asia by analyzing representations of war memories and transpacific and inter-Asian migrations in contemporary Asian American/Canadian and Asian cultural texts. Through examining how the selected literary texts, films, and creative nonfictions connect US wars in Asia with US anti-black racism at home, militarization and nuclearization in the Pacific, settler colonial violence, and postwar Asian state violence as entwined networks of complicity by the US, Asian states, and less recognizable Western imperial ally Canada, I argue that by reimagining US wars in Asia in relation to postwar violence in varied sites, the cultural texts complicate a US-centric understanding of the Cold War and Asian America. Adopting inter-Asian and transpacific frames, this dissertation on the one hand reframes the Cold War in relation to post-WWII violence in Asia and the Pacific, and, on the other hand, provides an alternative way of reading Asian American/Canadian and Asian cultural texts as mutual historical resources. The first three chapters analyze how Susan Choi’s The Foreign Student (1998), Don Mee Choi’s Hardly War (2016) and DMZ Colony (2020), Lee Issac Chung’s Minari (2020), and Bong Joon-ho’s Parasite (2019) interweave Korean War memories with Korean migration to the US and less recognizable atrocity committed by US-backed South Korean regimes within South Korea as well as in Jeju island and Vietnam. By examining how the texts depict US War in Korea in relational contexts of Japanese colonialism, South Korean state violence and subimperialism, and contemporary South Korea’s capitalist development, I argue that such relationalities elucidate historical atrocity doubly forgotten by both the US and South Korean nationalist narratives of the Korean War. The following three chapters examine how lê thi diem thúy’s The Gangster We Are All Looking for (2003), Ku Yu-ling’s Our Stories: Migration and Labour in Taiwan (2008/2011) and Return Home (2014), Madeleine Thien’s Dogs at the Perimeter (2005), and Ruth Ozeki’s A Tale for the Time Being (2013) illustrate Cold War afterlives in sites not commonly known as the frontstage of US wars in Asia. By grounding US wars in militarization and nuclearization in the Pacific and foregrounding Japan’s disavowal of war crimes and Canada’s complicity with US empire, obscuration of militarization and colonialism in Okinawa, and the explicit and implicit US presence in Taiwan and Vietnam, I argue that the texts help us further investigate historical atrocities that are intertwined with the more well-known US wars in Asia and yet rendered implicit. In addition to analyzing the entwined imperial networks in the texts, this dissertation also underscores how limits of the texts’ representation foreground the difficulties necessarily involved in comprehending and representing the Cold War. Through highlighting how the texts refuse to render traumatic memories into comprehensive narratives and instead attending to unlikely friendship and alliances, I show that imperial networks represented in these texts are not totalizing; rather, they generate, however briefly, relationalities forged by shared yet distinct histories and positionalities.

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)0.002
metaresearch head score (Gemma)0.003
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.144
Threshold uncertainty score0.286

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0170.019
Scholarly communication0.0100.006
Open science0.0010.004
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.016
GPT teacher head0.259
Teacher spread0.243 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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