Bibliographic record
Abstract
Women are doubly vulnerable to malnutrition, because of their high nutritional requirements for pregnancy and lactation and also because of gender inequalities in poverty. Undernutrition and overnutrition coexist in developing countries undergoing rapid nutrition transition, and women are susceptible to this double burden of "dysnutrition," often cumulating stunting or micronutrient malnutrition with obesity or other nutrition-related chronic diseases. The purpose of the present paper is to describe the adverse impact of income and gender inequities on women's nutritional health, and the dramatic consequences, not only for women themselves, but for children, families, and societies. Improving women's resources, including health, nutrition, education, and decisional power, is critical for equity and for the health of children and adults of future generations, since poor fetal and infancy nutrition is another risk factor for chronic diseases, in particular abdominal obesity, type 2 diabetes, hypertension, and cardiovascular disease. Addressing malnutrition and nutrition-related chronic diseases simultaneously is a challenge facing developing countries, and examples of promising initiatives are provided. Focusing on women along the lifecycle, according to the continuum of care approach, is essential to achieving the Millennium Development Goals and to breaking the intergenerational cycle of poverty, malnutrition, and ill-health.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".