Bibliographic record
Abstract
ABSTRACT Portes and Borocz's (1989 ) segmented assimilation framework argued that the assimilation of immigrants into American society does not necessarily or automatically lead to similarity and equality with the mainstream culture. Instead, endowed human capital, the nature of immigration, and reception contextualize the process and potentially lead to differential outcomes. Recognizing that spatial differences in assimilation may also exist, the segmented assimilation framework is extended within this paper to include a more explicit recognition of geography's role in shaping the assimilation trajectory. The empirical analysis draws upon the 1980 and 1990 PUMS data files, and compares the assimilation trajectory of Chinese immigrants (excluding Hong Kong and Taiwanese origins) across the New York, San Francisco, and Los Angeles metropolitan areas. Based upon period of arrival and age in 1980 and 1990, measures of assimilation are compared across these three metropolitan areas, along with the role of internal migration in maintaining or decreasing assimilation differences. The analysis indicates that the progress of assimilation varies significantly over space, with spatial differences in measures of assimilation persisting over time, despite the role of internal migration. Reasons as to why this occurs are presented in the conclusion.
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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 | 0.002 | 0.003 |
| 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) | 0.002 | 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".