Racial Diversity and Segregation: Comparing Principal Cities, Inner-Ring Suburbs, Outlying Suburbs, and the Suburban Fringe
Bibliographic record
Abstract
This article uses 2020 Census data to document recent trends in suburbanization, ethnoracial diversity, and residential segregation in the United States. It considers variation across inner-ring suburbs, outlying suburbs, and exurban areas at the metropolitan (metro) fringe. Suburbanization has recently continued, albeit more slowly than the 1990s and 2000s. Nearly two-thirds of all metro residents now live in the suburbs, fueled by change among ethnoracial minorities. For the first time, a majority of metro Blacks reside in suburbs. America's suburbs, especially inner-ring suburbs, have experienced extraordinary increases in racial diversity. Declines continue in metro segregation, and segregation remains lower in the suburbs than principal cities, especially in outlying and fringe areas. For suburban Asians and Hispanics, however, exposure to Whites has declined since 1990. The suburban fringe remains the least diverse component of metro America. The fringe is less segregated than other metro areas, but has experienced patterns (such as growing Black-White segregation) contrary to national trends.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".