Interracial Couples and Intergenerational Coresidence: The Role of Race/Ethnicity
Bibliographic record
Abstract
A sizeable share of interracial couples continue to report experiencing family opposition. However, few studies have empirically investigated whether intermarrying weakens ties with one’s kin and reduces exchanges between interracial couples and their family of origin. We examine whether interracial couples are less likely than same-race couples to live with an aging parent. We also investigate the role of each spouse’s race in shaping whether interracial couples reside with the wife’s or husband’s parents. Using data from the 2008-2022 American Community Surveys, we estimate logistic regression models to predict the odds of living with an aging parent. Next, we estimate multinomial logistic regression models to predict the competing risk of living with the wife's parents or husband's parents over the risk of living without an aging parent. Our study shows that interracial couples’ odds of living with any aging parents fall in between those of their same-race counterparts. Among White/Black couples, the risk of living with the parents of the White spouse exceeds the risk of living with the parents of the Black spouse. Among Black/Hispanic and White/Hispanic couples, the risk of living with the parent of the Hispanic spouse exceeds the risk of living with the parents of the other partner. Our findings detract from the view that intermarrying systematically weakens ties with the family of origin and reduces access to family support. Nonetheless, it also shows that race/ethnicity is an important correlate of interracial couples’ family exchange behavior.
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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 | 0.007 |
| 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 | 0.001 |
| 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".