Demographic Opportunity and the Mate Selection Process in India
Bibliographic record
Abstract
We merged individual-level data from the 2004-2005 India Human Development Survey with district-level data derived from the 1991 and 2001 Indian population censuses to examine how the numerical supply of men to which married women were exposed during late adolescence is associated with women's agency in the mate selection process and the duration of courtships. Multilevel models that control for an array of both individual and contextual factors showed that exposure to a relative surplus of potential mates is associated with a higher likelihood that women will have little or no say in the selection of their husband and an increased probability that women will meet their husband for the first time on their wedding day. Women's educational attainment, birth cohort, religion, caste, and region of residence also emerged as significant correlates of women's marital agency and courtship duration. The implications of these findings for India's growing sex ratio imbalance are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".