From Foreign Trainees to Unauthorized Workers: Vietnamese Migrant Workers in Japan
Bibliographic record
Abstract
This paper documents the experiences of Vietnamese migrant workers who migrated to Japan as industrial “trainees” during early and mid-2000, some of whom became unauthorized workers. Based on in-depth interviews and survey data collected in Vietnam in 2008 and 2009 from migrants who had returned from Japan, this paper explores the factors that led some workers to slip from legal to irregular work. Our analysis shows how the recruitment process in Vietnam, the implementation of the trainee/intern program in Japan, and migrant networks together create the conditions that encourage or discourage some workers from working in the irregular job sector and overstaying their visas. We situate this argument within the conceptualization of unauthorized labor migration as a structurally embedded phenomenon.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".