Multiracial Children's Experiences of Family Instability
Bibliographic record
Abstract
Abstract Objective This study compares multiracial and monoracial children's exposures to family instability, attending to variation by parents' marital status at birth. Background Previous research has revealed considerable racial/ethnic heterogeneity in children's exposure to family instability. Adding to this diversity is the rising share of multiracial and multiethnic children. Yet, multiracial and multiethnic children's experiences of family instability remain largely unexamined. Methods Data come from the 2006–2019 National Survey of Family Growth, a nationally representative repeated cross‐section of U.S. adults of reproductive age. The analytic sample included 15,369 children born in first marriages and 8,612 children born in first cohabitations to non‐Hispanic White, non‐Hispanic Black, and Hispanic parents. Multistate life tables, negative binomial regression, and multinomial logistic regression were used to compare the number of family transitions and family trajectories of multiracial and monoracial children through age 12. Results Differences in exposures to family instability between multiracial and monoracial children varied by parents' marital status at birth. Multiracial children born to cohabiting parents generally experienced more childhood family instability than their monoracial counterparts. Conversely, the family experiences of multiracial children born in first marriages typically fell between those of their monoracial counterparts. Conclusion Given that higher shares of multiracial children are born within the context of cohabitation relative to their monoracial peers, these findings suggest that multiracial children may be particularly vulnerable to experiencing family instability.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".