Does Socioeconomic Status Matter? Race, Class, and Residential Segregation
Bibliographic record
Abstract
Spatial assimilation theory predicts that racial and ethnic residential segregation results at least in part from socioeconomic differences across groups. In contrast, the place stratification perspective emphasizes the role of prejudice and discrimination in shaping residential patterns. This article evaluates these perspectives by examining the role of race and class in explaining the residential segregation of African Americans, Hispanics, and Asians from non-Hispanic whites in all U.S. metropolitan areas over the 1990 to 2000 period. Using the dissimilarity index and various indicators of socioeconomic status (SES), we find that in both 1990 and 2000 high-SES racial and ethnic groups were significantly less segregated from non-Hispanic whites than corresponding low-SES groups, especially among Hispanics and Asians—much as the spatial assimilation model would predict. Consistent with the place stratification model, African Americans of all SES levels continued to be more segregated from whites than were Hispanics and Asians, and this changed little between 1990 and 2000. However, the importance of SES in explaining the segregation of African Americans from whites increased over the period, while not for Hispanics and Asian Americans, providing support for a modest increase in the applicability of the spatial assimilation model for African Americans in the 1990s.
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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 | 0.006 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".