Bibliographic record
Abstract
This paper examines how three serial migrants—Yoshi, Natalia, and Eric—who have repeatedly migrated since childhood, build relationships with others amidst experiences of inclusion and exclusion in their host societies, through their life stories. Serial migrants are defined as individuals who migrate two or more times for various purposes (e.g., accompanying parents, education, employment, wandering, or settlement) without being rooted in a specific place. While traditional migration studies often assume a connection to the place of origin, this paper explores the formation of complex identities beyond such frameworks. Yoshi, born in Korea, moved to Japan, South Africa, and the U.S., facing marginalization as an "Asian" in South Africa's racially hierarchical society. He underwent double-eyelid surgery to assimilate with white people, but this led to self-loathing and internalized racism. Natalia, raised between Japan and Sweden, later migrated to Canada, Guatemala, Italy, and Hong Kong. Seen as a "foreigner" in both nations, she adapted her name and behavior. Eric, who moved across Argentina, Japan, Indonesia, Mexico, and Brazil, overcame class-based exclusion at a Brazilian military academy by forming external ties. Through "compromise," a mutual adjustment process, the three displayed a cosmopolitan attitude, accepting others' differences while transforming themselves. Yoshi performed a "Christian" identity, Natalia adjusted her name to be more "Japanese," and Eric built new relationships to cope with constraints. However, "compromise" has limits: Yoshi struggled with self-loathing, Natalia faced stereotypes, and Eric couldn't resolve school exclusion. This study shows that serial migrants' experiences reflect the complex realities of mobility, revealing cosmopolitanism as a process of practical compromise and conflict, not mere tolerance. Their diverse lives offer new insights into identity formation independent of origins, enhancing our understanding of human mobility.
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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 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".