Multivariate explanation of the 1985–1990 and 1995–2000 destination choices of newly arrived immigrants in the United States: the beginning of a new trend?
Bibliographic record
Abstract
Abstract This paper identifies the salient features in the 1985–1990 and 1995–2000 destination choices of newly arrived immigrants, and performs multivariate explanation of these choices, based on an application of a multinomial logit model to the state‐specific immigration data of the 1990 and 2000 censuses. The salient features are that: (1) the destination choice pattern of the newly arrived immigrants became more dispersed from the late 1980s to the late 1990s; (2) the change was pervasive in the sense that it was true for all combinations of five broad ethnic groups and four levels of educational attainment; (3) the change was much greater for Hispanics and Blacks than for Asians and Whites; (4) the lower the level of education, the greater the increase in dispersion; and (5) the Hispanics with the lowest education experienced the greatest increase in dispersion. Our multivariate analysis reveals that: (1) while the attraction of co‐ethnic communities as destinations remained strong for both periods, it became much less intense in the late 1990s, especially for Hispanics and Blacks; (2) the newly arrived immigrants were subject to the strong pull of higher income level in both periods; (3) the pull of employment growth became stronger and more industry‐specific from the late 1980s to the late 1990s; and (4) the pull of service employment growth, especially for the least‐educated Hispanic immigrants, became much stronger in the later period. In the context of the progressive entrenchment of neoliberalism and the major changes in immigration policies, our empirical findings suggest that the ethnically selective dispersal of immigrants in the late 1990s is probably the beginning of a new trend. Copyright © 2007 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".