Global, Regional and Country Trends in Underweight and Stunting as Indicators of Nutrition and Health of Populations
Bibliographic record
Abstract
Stunting and wasting provide indicators of different nutritional deficiency problems, the causes of which are well established. Underweight based on weight-for-age cannot distinguish between these two and is therefore not useful to target programs and has limited value for tracking progress. Stunting reduces later school attainment and income as adults and increases the risk of obesity and noncommunicable diseases in later life. Globally, the estimated number of stunted children is decreasing, but is not on track to meet the goal of 100 million by 2025 (165 million), and there has been little change in the number of children suffering from wasting since 2004. Stunting and wasting provide excellent indicators of inequity. For example, from 1990 to 2010, the number of stunted children in Asia declined from 188.7 to 98.4 million, while in sub-Saharan Africa there was essentially no change in prevalence, and the number of stunted children increased from 45.7 to 55.8 million. Recent global development movements are recognizing the need for robust measures of trends in nutritional status of children, particularly during the critical first years of life. Such measures are needed to track progress and improve accountability, and should be aspirational to mobilize sufficient investment in nutrition.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".