Diverging stories of son preference in South Asia: a comparison of India and Bangladesh
Bibliographic record
Abstract
In Bangladesh, overall sex ratio has declined from 109.6 (males/females) in the \n1950s to 100.3 in 2011. Unlike countries with female deficits, the improvement in \nsex ratio has extended to the under‐5 age group. This has happened in a context \nwhere per‐capita income has grown modestly but poverty continues to be \nwidespread. Thus the story of “missing women” is evolving differently in \nBangladesh than from India where decline in overall sex ratios has been \naccompanied by worsening of child sex ratios. In this paper we explore the \nhypothesis that improvement in child sex ratios in Bangladesh is due to a shift in \nparental preferences about sex composition of families in a society undergoing \nrapid socio‐economic change. Using a combination of quantitative and qualitative \ndata, we find that parents are less likely to discriminate between sons and \ndaughters than in the past with respect to survival and investments in human \ncapital. These changes indicate a weakening of patriarchal structures and cultural \nnorms around fertility intentions and sex composition of families. In comparison \nto India, it is speculated that the diverging story of sex preference in Bangladesh \ncould be related to the timing of introduction of sex selection technology and the \nrole of the state and civil society in the two contexts.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".