Bibliographic record
Abstract
Fertility depends on household decisions in Malaysia; and, in turn, these decisions are strongly influenced by economic and socio-economic factors. Currently, fertility levels around the world vary according to the intergenerational relationships, the socio-economics statuses, and the socio-demographic characteristics of a particular nation. In general, more industrialized and economically developed societies have lower fertility than less-developed societies do. Groups that are more educated and earn higher incomes have lower fertility than less-educated groups with lower incomes do. The purpose of this paper is to dicuss the development of the empirical model to identify the principal determinants of fertility in Malaysia. The results stemmed from the use of panel data that is obtained from the Minnesota Population Centre, the Integrated Public Use Microdata Series, and international data provided by the Department of Statistics, Malaysia. In the empirical analysis, count models are employed. The findings show that marital status, owning a house, and households having women of child-bearing age all affect fertility decisions. In addition, social characteristics, such as ethnicity, religion, socio-economic status, and education level, affect household’s fertility decision.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".