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Record W6923699692 · doi:10.14989/pnggw_16_2

Migration and Memory: Revisiting the narratives of post-war hibakusha immigrants

2023· article· en· W6923699692 on OpenAlexaboutno aff

Bibliographic record

VenueInstitutional Repositories DataBase (IRDB) · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicJewish and Middle Eastern Studies
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationNarrativeRefugeeEthnic groupIdentity (music)

Abstract

fetched live from OpenAlex

The end of the Second World War pushed individuals from Japan to migrate to other countries driven by their traumatic experiences of the war and its aftermath. Among them are the hibakusha—victim-survivors of the atomic bomb—who fled Hiroshima and Nagasaki after 1945. These post-war hibakusha immigrants sought a new life in Brazil, Canada, Mexico, and the United States, while some also became return migrants to their homeland in Korea. Migration posed new challenges for the post-war hibakusha immigrants due to both their status as migrants and their history as victims of nuclear warfare. As they continued to share narratives of their war experience, they also confronted their identity as migrants in a country which might bear a bitter history with Japan. This research looks into the narratives by post-war hibakusha immigrants as they appear in online collections like Hibakusha Stories and the archives of Asahi Shimbun dedicated to hibakusha narratives: So Tell Me About Hiroshima and Notes from Nagasaki. It analyzes how the hibakusha’s personal history as victim-survivors intersect with their identity as immigrants and thus shape their narratives of the war and their view of Japan and their country of migration. It argues that efforts of hibakusha activism promote new opportunities and directions for dialogue about the war and its global impact.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.544
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.028
GPT teacher head0.294
Teacher spread0.265 · 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 teacher head, not a consensus.

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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