Bibliographic record
Abstract
Objective: This paper examines racial and ethnic differences in fertility, emphasizing the fertility differentials between Asian American women and their counterparts in other racial/ethnic groups. Background: By 2055, Asian Americans will become the largest immigrant group in the United States. The fertility behavior of Asian American women will have significant implications for the future size and composition of the US population. Yet, due to data constraints, little is known about Asian American fertility. Method: This paper analyzes data from the 2022─2023 American Community Survey to compare the odds of having a child in the past year by women’s race/ethnicity, education, and marital status.Results: Fertility differences between Asian American women and those in other racial/ethnic groups were primarily due to differences in non-marital fertility rates. For unmarried women in all groups, having a college degree was associated with lower adjusted odds of having a child during the past 12 months. However, this educational differential was largest among Asian American women. Having a college degree was associated with higher adjusted odds of having a birth for married White, Black, and Hispanic women. Educational differences were minimal among married Asian American women. Conclusion: Asian American women with college degrees display a stronger preference for marriage as the context for childbearing than women in other racial/ethnic and educational groups.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".