Improving Child Nutritional Status in Order to Fill the Demographic Dividend in East Java Province, Indonesia
Bibliographic record
Abstract
All Indonesian children are national assets where the future of the nation depends on their quality. East Java Province experienced a demographic bonus period and the peak occurred in 2019 and a third of the population of East Java were children aged 0-17 years. Now the government of East Java Province has implemented five strategies in dealing with demographic bonuses, namely improving the quality of youth human resources, creating quality human resources, placing the elderly population as assets, improving health efforts, and economic empowerment. In the strategy of increasing health efforts, it is necessary to evaluate the nutritional status of children and toddlers. Improving the nutritional status of the community is one of the efforts that has a significant impact and is one of the determining factors for improving the quality of human resources. At the individual level, nutritional conditions are influenced by nutritional intake and related infectious diseases. The first two years of life is a critical period, if there are nutritional disorders in this period, the impact is permanent.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".