Does Socioeconomic Status Matter? Race, Class, and Residential Segregation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it