Why Does Women’s Empowerment In One Generation Not Lead To Later Marriage and Childbearing In The Next?: Qualitative Findings from Bangladesh
Bibliographic record
Abstract
This study uses qualitative data to explore the socio-cultural processes through which women influence two proximate determinants of health and well-being-age at marriage and age at initiation of childbearing-in the next generation, and to investigate social processes that may be undermining women’s empowerment and its effects across generations. Open-ended, in-depth interviews were conducted with triads of womenyoung married women, their mothers, and their mothers-in-law-with one of the senior women in each triad having been identified as comparatively empowered based on a set of indicators measured in a prior survey. The findings suggest the cultural meanings and social dynamics of marriage in the context of poverty and vulnerability to economic crisis are persistent constraints to later marriage and childbearing even in families of empowered women who are aware of the risks and disadvantages of early marriage and childbearing. No substantial differences were found between empowered versus unempowered women and their families in the ways that decisions about the timing of marriage and childbearing were made, nor, in general, between mothers versus mothers-in-law.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".