Biosocial life‐course factors associated with women's early marriage in rural India: The prospective longitudinal Pune Maternal Nutrition Study
Bibliographic record
Abstract
OBJECTIVES: By convention, women's early marriage is considered a sociocultural decision sensitive to factors acting during adolescence such as poverty, early menarche, and less education. Few studies have examined broader risk factors in the natal household prior to marriage. We investigated whether biosocial markers of parental investment through the daughters' life-course were associated with early marriage risk in rural India. We used an evolutionary perspective to interpret our findings. MATERIALS AND METHODS: A prospective cohort recruited mothers at preconception. Children were followed from birth to age 21 years. Multivariable logistic regression models estimated odds ratios of marrying early (<19 years) associated first with wealth, age at menarche and education, and then with broader markers of maternal phenotype, natal household characteristics, and girls' growth trajectories. Models adjusted for confounders. RESULTS: Of 305 girls, 71 (23%) had married early. Early married girls showed different patterns of growth compared to unmarried girls. Neither poverty nor early menarche predicted early marriage. Girls' non-completion of lower secondary school predicted early marriage, explaining 19% of the variance. Independent of girls' lower schooling, nuclear household, low paternal education, shorter gestation, and girls' poor infant weight gain were associated with marrying early, explaining in combination 35% of the variance. DISCUSSION: Early marriage reflects "future discounting," where reduced parental investment in daughters' somatic and educational capital from early in her life favors an earlier transition to the life-course stage when reproduction can occur. Interventions initiated in adolescence may occur too late in the life-course to effectively delay women's marriage.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".