The Predictive Model of the Fertility Pattern of Young Women (15-24 Years Old) In South Sulawesi, Indonesia
Bibliographic record
Abstract
Teenagers that have given birth have a high chance of a total fertility rate and prevalence. The study aimed to analyze the contribution of demographic and socio-economic factors, access to information, sexual activity, and literacy on family planning on the fertility pattern of young women (15-24 years old). This research uses 2017 data from the Indonesian Demography and Health Survey (IDHS). Data analysis performed multiple logistic regression with a predictive model. The predictors of young female fertility (15-24 years old) were marital status (aOR: 373.9, 95%CI 112.7-1239.8), age of 19-21 years old (aOR: 7.74, 95%CI 2.19-27.32), age of 22-24 years old (aOR: 4.79, 95%CI 1.61-14.32), a low education level (aOR: 2.53, 95%CI 0.94-6.82), unemployed (aOR: 2.73, 95%CI 1.14-6.55) or working in agriculture (aOR: 1.16, 95%CI 0.19-6.87), and low (aOR: 1.79, 95%CI 0.73-4.41) or medium (aOR: 1.58, 95%CI 0.42-5.87) wealth index, based on SKDI's 2017 data. There needs to be an improvement in the education access to increase job opportunities and improve the socio-economic conditions of the community. This improvement will have positive impacts in preventing adolescent marriage and decreasing the fertility rate of young women
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".