Bibliographic record
Abstract
The decision on the number of children is taken within the household decision making framework. One argument states that, the rising costs and declining economic value of children drives couples to go for smaller families. The other major economic argument states that, it is in parent’s best interest to have large number of children in any agrarian or poor economic setting, where children can be put to work to contribute to the household income. Therefore, in this study an attempt has been made to capture the economic effects in a poor state of India i.e. Odisha, where sustained fertility decline has occurred in spite of unfavourable conditions. Data for the study are drawn from National Family Health Survey and also from a primary survey carried in one district of Odisha. The result demonstrated that the desire to stop child bearing increases rapidly with the number of living children. Multiple classification analysis shows that caste, educational level, standard of living of the household, exposure to mass media and number of living son are important determinants of desire family size. It is also evident that in spite of widespread poverty in the state the economic provision of the government is not a precondition for the decision of number of children. The result show that the thresholds have fallen because of the changing value of children; cost of raising them has increased and child participation in work force has decreased. Such changes have occurred because of diffusion of ideas.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".