Assimilation—On (Not) Turning White: Memory and the Narration of the Postwar History of Japanese Canadians in Southern Alberta
Bibliographic record
Abstract
This article explores understandings of “race”—specifically, what it means to be Japanese—of nisei (second generation) individuals who acknowledge their near complete assimilation structurally and normatively into the Canadian mainstream. Examining historically contextualized analyses of memory fragments from oral history interviews conducted between 2011–17, the article focuses on the voices and experiences of southern Alberta, an area whose significance to local, national, continental, and trans-Pacific histories of people of Japanese descent is belied by a lack of dedicated scholarly attention. In this light, the article reveals how the fact of being Japanese in the latter half of the twentieth century was strategically central to nisei lives, both as individuals and in their communities. In imagining a racial hierarchy whose apex they knew they could never share with the hakujin (whites), the racial heritage they nevertheless inherited, and would bequeath, could be so potent as to reverse the direction of the colonial gaze with empowering effects in individual engagements then and as remembered now. We see how the narration and validation of one’s life is the navigation of wider historical contexts, the shaping of the postcolonial legacy of imperial cultures as Britain and Japan withdrew from their erstwhile colonial projects in Canada.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.031 | 0.020 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 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".