BIRTH ORDER, STAGE OF INFANCY AND INFANT MORTALITY IN INDIA
Bibliographic record
Abstract
Using data from India's National Family Health Survey, 2005-06 (NFHS-3), this article examines the patterns of relationship between birth order and infant mortality. The analysis controls for a number of variables, including mother's characteristics such as age at the time of survey, current place of residence (urban/rural), years of schooling, religion, caste, and child's sex and birth weight. A modest J-shaped relationship between birth order of children and their risk of dying in the neonatal period is found, suggesting that although both first- and last-born children are at a significantly greater risk of dying compared with those in the middle, last-borns (i.e. fourth and higher order births) are at the worst risk. However, in the post-neonatal period first-borns are not as vulnerable, but the risk increases steadily with the addition of successive births and last-borns are at much greater risk, even worse than those in the neonatal period. Although the strength of relationship between birth order and mortality is attenuated after the potential confounders are taken into account, the relationship between the two variables remains curvilinear in the neonatal period and direct in the post-neonatal period. There are marked differences in these patterns by the child's sex. While female children are less prone to the risk of dying in the neonatal period in comparison with male children, the converse is true in the post-neonatal period. Female children not only run higher risks of dying in the post-neonatal period, but also become progressively more vulnerable with an increase in birth order.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".