Bibliographic record
Abstract
Focusing on the situation in western Canada, this paper aims to investigate the participation of Japanese migrants in the established Nikkei community. Within the long history of research on prewar Japanese migrants and their generations of descendants, attention has been paid primarily to ethnicity and acculturation. However, the increase of Japanese migrants since the 1990s has started to generate academic interest. Within the Canadian Nikkei community, those who left Japan after 1967 are called ijusha. In recent studies, their motivations for migration, interactions with both other ijusha and the established Nikkei community, and their notions of nationality and ethnicity have been considered. While these considerations have taken it as self-evident that contact between ijusha and the Nikkei community is limited, the reasons for the estrangement between these two groups has not been adequately explored. Ijusha are often unwilling to participate in Nikkei activities, and instead tend to merge directly into Canadian society by utilizing their high occupational skills and language ability. In this paper, I use data collected through participant observation and interviews with ijusha to discuss their sense of cultural belonging. As a result of this research, I have identified four factors that contribute to the gap between ijusha and the Nikkei community. They are 1) a denial of Japan, 2) de-abroadization, 3) the media, and 4) lifestyle migration and plural homes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".