Social networks and selectivity in Brazilian migration to Japan and the United States
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This research analyses the composition of Brazilian migrants in two case studies, comparing the demographics of first‐time migrants over time in the network between Maringá, Brazil and Japan with that between Criciúma, Brazil and the US. Couched primarily within migrant social network theory, the research explores how the legal framework operating in each case influences the level and composition of Brazilian migration over time. Brazilian migration to Japan generally occurs within the context of a legally regulated ‘ethnic‐return’ guest worker program, whereas Brazilian migration to the US is largely unauthorised. The research shows that social networks do operate to diversify the migrant demographic composition over time in both migration flows. However, the development of and dependence on social networks appears stronger in migration to the US (at least initially), which suggests a relation between the legal context of the migration flow and the form and strength of its social networks. Copyright © 2010 John Wiley & Sons, Ltd.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it