Poverty and Affluence across the First Two Generations of Voluntary Migration from Africa to the United States, 1990–2012
Bibliographic record
Abstract
The first substantial waves of voluntary migration from Africa arrived in the United States in the last quarter of the twentieth century. The largest number of them hailed from Egypt, Ethiopia, Nigeria, and South Africa. Highly select in their educational aspirations and achievements, many of them settled and started families. By 2010, their U.S.-born children had begun to reach adulthood, offering us a first look at intergenerational mobility among voluntary migrants from Africa. The racial diversity in this group of immigrants allows us to gauge the impact of racial stratification on immigrant adaptation. 1990 U.S. census and 2008–2012 American Community Survey data are used to uncover patterns of affluence and poverty among young Egyptian, Ethiopian, Nigerian, and South African immigrants in 1990 and U.S.-born men and women of those ancestries in 2008–2012. White and Black cohorts of U.S. birth and stock serve as additional referents. I find that women of the African second generation have advanced faster than their male counterparts and that racial group membership is at least predictive of financial well-being as specific national origins, with Black Africans, and Ethiopians in particular, showing pronounced disadvantages compared with White Africans in both the immigrant and second generations.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".