Migration and Memory: Revisiting the narratives of post-war hibakusha immigrants
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.017 | 0.014 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".