Does It Matter What She Wants? The Role of Individual Preferences Against Unmarried Motherhood in Young Women’s Likelihood of a Nonmarital First Birth
Bibliographic record
Abstract
Most young people in the United States express the desire to marry. Norms at all socioeconomic levels posit marriage as the optimal context for childbearing. At the same time, nonmarital fertility accounts for approximately 40 % of U.S. births, experienced disproportionately by women with educational attainment less than a bachelor's degree. Research has shown that women's intentions for the number and timing of children and couples' intent to marry are strong predictors of realized fertility and marriage. The present study investigates whether U.S. young women's preferences about nonmarital fertility, as stated before childbearing begins, predict their likelihood of having a nonmarital first birth. I track marriage and fertility histories through ages 24-30 of women asked at ages 11-16 whether they would consider unmarried childbearing. One-quarter of women who responded "no" in fact had a nonmarital birth by age 24-30. The ability of women and their partners to access material resources in adulthood were, as expected, the strongest predictors of the likelihood of nonmarital childbearing. Nonetheless, I find that women who said they would not consider nonmarital childbearing had substantially higher hazards of fertility postponement and especially of marital fertility, even after controlling for race/ethnicity, mother's educational attainment, family of origin intactness, self-efficacy and planning ability, perceived future prospects, and markers of own educational attainment and work experience into early adulthood.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".