Bibliographic record
Abstract
One of the myriad horrors of nuclear weapons is that they kill without discrimination. People, by contrast, are habitual discriminators, and so the human victims of such an indiscriminate assault will experience differential treatment in its aftermath. This bleak paradox lies at the heart of Naoko Wake's study of Japanese American and Korean American hibakusha, the survivors of the atomic bombings of Japan. Built upon oral interviews with U.S. citizens of Japanese ancestry who were in Hiroshima or Nagasaki in 1945, as well as those from Japan and Korea who relocated to the United States following the war, American Survivors tells the story of bombing victims who fought for their lives, for recognition, for compensation, and for justice, and the ways that their gender, ethnicity, class, and composite nationality has affected this nearly eight-decade struggle. Wake reveals the deep connections forged by travel, migration, and familial ties among people throughout the transpacific region, and emphasizes how cosmopolitan both targeted cities were, Hiroshima in particular. As the prefecture that sent the highest number of immigrants to the United States, Hiroshima had profound ties to the nation that would annihilate it, and thousands of Japanese Americans would become casualties of the bombings. “Imagined as targeting a single nation or people,” Wake writes, “the weapon in fact exploded upon a diversity of cultures created by the cities of immigrants” (p. 36). When American survivors returned to the United States to reconnect with families, and when other bomb victims followed as war brides or economic migrants, they found a nation basking in its triumph in the “good war” and its Cold War status as global defender of freedom. The task of convincing the government and their fellow Americans that hibakusha were indeed victims deserving redress would prove daunting.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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.
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".