Stunting : Studi Konstruksi Sosial Masyarakat Perdesaan dan Perkotaan Terkait Gizi dan Pola Pengasuhan Balita di Kabupaten Jember
Bibliographic record
Abstract
Abstract: Indonesia’s rank in world was 5th on stunting case. 5 million of children under five (38.6 % from 12 milion) got stunting in Indonesia. The aims of this study were to descript the social construction of rural-urban community about the meaning of children’s health and illness,and the pattern of nurturing which was related to stunting. The study used qualitative’s method, datas collected with depth interview and observation partisipation. The study was conducted in rural-urban communities which had stunting cases in Jember (Kalisat and Jelbuk). The study was conducted in June to December 2013. The study showed that stunting were related to social construction. Difference social construction in rural-urban which constructed the meaning of healthy or illness and nuruturing the stunting’s children was affected by maternal education, early-age marriage, after marriage’s residence, responsibilities of nurturing, and valuable concept in community that causes the lack of knowledge about nutrition. The study concluded that stunting was not a single cause of heatlh’s problems, but it related to social construction. Causes lied in the distinction of social construction, patterns of communication and interpretation between health providers and community, so there was no meeting point for the success of nutritional improvement children under five’s programs.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".