Scaling Up Nutrition: What Will It Cost?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Undernutrition imposes a staggering cost \n worldwide, both in human and economic terms. It is \n responsible for the deaths of more than 3.5 million children \n each year (more than one-third of all deaths among children \n under five) and the loss of billions of dollars in forgone \n productivity and avoidable health care spending. Individuals \n lose more than 10 percent of lifetime earnings, and many \n countries lose at least 2-3 percent of their gross domestic \n product to undernutrition. The current economic crisis and \n its potential impact on the poor make investing in child \n nutrition more urgent than ever to protect and strengthen \n human capital in the most vulnerable developing countries. \n This report offers suggestions on how to raise these \n resources. It is an investment we must make. It will yield \n high returns in the form of thriving children, healthier \n families, and more productive workers. This investment is \n essential to make progress on the nutrition and child \n mortality Millennium Development Goals (MDGs) and to protect \n critical human capital in developing economies. The human \n and financial costs of further neglect will be high. This \n call for greater investment in nutrition comes at a time \n when global efforts to strengthen health systems provide a \n unique opportunity to scale up integrated packages of health \n and nutrition interventions, with common delivery platforms, \n and lower costs. The report has benefited from the expertise \n of many international agencies, nongovernmental \n organizations, and research institutions. The cooperation of \n so many practitioners is evidence of a growing recognition \n of the need to invest in nutrition interventions, and a \n growing consensus about how to deliver effective programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it