Identifying Potential Factors of Childbearing in Bangladesh
Bibliographic record
Abstract
This paper aims to identify different potential factors associated with childbearing pattern among the ever-married women in Bangladesh. Childbearing pattern is directly related to fertility level and rapid population growth is the major consequence of more childbearing. Bearing more children affects adversely on social and economic opportunities and produces substantial risks to the health of mothers and children. Bangladesh Demographic Health Survey data 1999-2000 and 2004 have been used for this study. First bi-variate analysis method is carried out to identify different factors associated with childbearing. Then generalized linear modelling approach has been performed to quantify the simultaneous effect of key socio-economic and demographic factors. Our primary findings show that childbearing varied tremendously by education level and age at first marriage. From the generalized linear model analysis, mother's age group, types of place of residence, division, media exposure are found to be significantly associated with bearing more children among the ever-married women in Bangladesh. These findings suggest that government should continue its effort to ensure higher education for females and to promote to delay age at marriage.
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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) | 0.003 | 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".