Ethnicity and the Immigration of Highly Skilled Workers to the United States
Bibliographic record
Abstract
Purpose - This paper aims to examine ethnicity among highly skilled immigrants to the USA. Design/methodology/approach - The paper examines five classic components of ethnicity – country of birth, race, skin color, language, and religion – among persons admitted to legal permanent residence in the USA in 2003, as principals in the three main employment categories (EB‐1, EB‐2, and EB‐3), using data collected in the US New Immigrant Survey. Findings - The visa categories have distinctive ethnic configurations. India dominates EB‐2, European countries and Canada EB‐1. The ethnicity portfolio contains more languages than religions. Language is shed before religion, and religion may not be shed at all, except among the ultra highly skilled of EB‐1. Highly skilled immigrants are mostly male; they are not immune from lapsing into illegality; they have a shorter visa process than their cohortmates; smaller proportions than in the cohort overall intend to remain in the USA. Larger proportions in EB‐2 and EB‐3 sent remittances than in the cohort overall. A little measure of assimilation – using dollars to describe earnings in the country of last residence, even when requested to use the country's currency – suggests that highly skilled immigrants are more likely to “think in dollars” than their cohortmates. Research limitations/implications - The paper is like an aerial reconnaissance. It is necessary to now go under the ledges and into the caves. Originality/value - The data used are the first ever collected on a probability sample of new legal immigrants to the USA. It is expected that many researchers will use these data to generate valuable new knowledge.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".