Challenges of Nikkei Peruvian Second Generation in Japan: An Overview of Their Employment Status after the Lehman Shock 2008
Bibliographic record
Abstract
This article analyzes the second generation of Nikkei Peruvians who are residing in Japan to overcome their employment status from the first generation of Nikkei unskilled workers. Using the mix methodology (Qualitative & Quantitative) the paper describes the main characteristic of Nikkei Peruvian second generation after the financial crisis 2008, and focus in the main variables that influence their differences in employment status. The study involved the interview made since 2009 from the first generation Nikkei Peruvian to the second generation Nikkei Peruvian during 2015. They were asked to answer a socio-economic questionnaire and deep interview. Results of the study demonstrate that the main characteristics for Nikkei Peruvian second generation living in Japan are gender, civil status, place of birth, age group, study, social aid and employment status. Unlike the first generation unskilled workers the second generation employment status differs case by case due to their Japanese background. Even though some percentages of second generation continue as unskilled workers in Japanese factories even their social and human capital differs from the first generation of Nikkei Peruvian. The main differences between factory and no factory workers have to do with their civil status, age group, study status, and social aid. The second generation with background of two cultures and two languages who become unskilled workers means that their human and social capital as a bridge of two cultures will be devalued that would be consider by local government.
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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 | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".