CASTE DIFFERENTIALS IN DEATH CLUSTERING IN CENTRAL AND EASTERN INDIAN STATES
Bibliographic record
Abstract
This study assessed caste differentials in family-level death clustering, linked survival prospects of siblings (scarring) and mother-level unobserved heterogeneity affecting infant mortality risk in the central and eastern Indian states of Jharkhand, Madhya Pradesh, Odisha and Chhattisgarh. Family-level infant death clustering was examined using bivariate analysis, and the linkages between the survival prospects of siblings and mother-specific unobserved heterogeneity were captured by applying a random effects logit model in the selected Indian states using micro-data from the National Family Health Survey-III (2005-06). The raw data clustering analysis showed the existence of clustering in all four states and among all caste groups with the highest clustering found in the Scheduled Castes of Jharkhand. The important factor from the model that increased the risk of infant deaths in all four states was the causal effect of a previous infant death on the risk of infant death of the subsequent sibling, after controlling for mother-level heterogeneity and unobserved factors. The results show that among the Scheduled Castes and Scheduled Tribes, infant death clustering is mainly affected by the scarring factor in Jharkhand and Madhya Pradesh, while mother-level unobserved factors were important in Odisha and both (scarring and mother-level unobserved factors) were key factors in Chhattisgarh. Similarly, the Other Caste Group was mainly influenced by the scarring factor only in Odisha, mother-level unobserved factors in Jharkhand and Chhattisgarh and both (scarring and mother-level unobserved factors) in Madhya Pradesh. From a government policy perspective, these results would help in identifying high-risk clusters of women among all caste groups in the four central and eastern Indian states that should be targeted to address maternal and child health related indicators.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".