The case of Dr Masajiro Miyazaki: Japanese-Canadian healthcare in World War II
Bibliographic record
Abstract
The forcible relocation of Japanese-Canadians (Nikkei) during World War II has been widely examined; however, little scholarly attention has been paid to the impact of relocation on the medical services provided to, and by, the Nikkei. This article highlights the issue of providing sufficient medical care during forcible relocation and the experiences of one Nikkei physician, Dr Masajiro Miyazaki. His story illustrates both the limitations in the healthcare provided to the Nikkei community during relocation and the struggle for Nikkei medical professionals to continue their practice during the war. The agency of the Nikkei-who constantly balanced resistance and adaptation to oppressive conditions-comes to the forefront with this case study. Dr Miyazaki's personal records of forcible relocation, as well as his published memoir, reveal aspects of the lived reality of one Nikkei physician who was not included in the government discourse, or in the dialogue among his fellow Nikkei physicians, such as inter-racial medical care. It is evident through this case that there was great diversity in the level of medical care which the Nikkei received during their relocation in Canada. Furthermore, Dr Masajiro Miyazaki's story proves that healthcare professionals, from doctors to nurses' aides who were both Nikkei and white, provided extraordinary medical services during the forcible relocation, despite significant constraints.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.066 | 0.015 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".