Black, White, or Multiracial? How Socioeconomic and Political Context Shapes Racial Classification
Bibliographic record
Abstract
In this study, the authors examine whether and how city-level socioeconomic and political context shape racial classification practices in U.S. Black-White families. The authors link IPUMS 2012–2022, American Community Survey population estimates, and presidential election voting estimates to create a dataset that has 19,907 children across 1,135 Public Use Microdata Areas. Results from multilevel multinomial logistic regression models show that although overall parents are most likely to classify their multiracial children as Black and White, parents are more likely to classify their children as a single race in certain sociopolitical contexts: compared with parents in wealthier areas, parents in disadvantaged areas are more likely to classify their children as only White. Compared with parents in predominantly Democratic areas, parents in predominantly Republican areas are more likely to classify their children as only Black. The authors argue that parents of Black-White children promote racial passing in a low-wage labor market, and armoring in anti-Black political contexts. These findings emphasize the salient role of socioeconomic and political context in the construction of race and contribute to ongoing debates on the future of race and ethnicity in the United States.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".