Son Preference and Child Under nutrition in the Arab Countries: Is There a Gender Bias against Girls?
Bibliographic record
Abstract
Although son preference has been demonstrated in the MENA region with different manifestations and at several phases of human development, the literature remains sparse as far as studies examining the early childhood phase are concerned. The current study aims to explore the presence of a gender bias in child nutrition status and its association with maternal son preference in three Arab countries; namely, Egypt, Jordan, and Yemen. Child nutritional status is measured using the Height-for-Age z-score (HAZ). To examine the presence of gender bias across the entire nutritional distribution, we utilized a quantile regression framework. We use data from the most recent rounds of the Demographic and Health Survey on a nationally representative sample of children aged 0–4 years. Descriptive statistics show that 21.5% of the mothers demonstrate son preference in Yemen compared to 19.10% in Jordan and 13.26% in Egypt. Results of the baseline OLS model demonstrate a robust pro-girl nutrition bias in the three countries. However, results of the quantile regression model show that this pro-girl nutrition bias is only prevalent at the lower segment of the conditional HAZ distribution for Jordan and Yemen and is prevalent across the whole conditional HAZ distribution for Egypt. We also find no statistically significant association between maternal son preference and gender bias in child nutrition in the three countries. Although son preference is manifested in several phases of human development in the MENA region, the current study finds no nutritional bias against girls in the examined countries at early childhood.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".