Bibliographic record
Abstract
In poor countries, over a quarter of children under the age of five years are malnourished. The corresponding rate in rich countries is below 3%. Unfortunately, being undernourished as a child is associated with worse economic outcomes as an adult, largely a result of worse adult health. Thus, malnutrition among children creates one of the starkest discrepancies in individual well-being between rich and poor countries. Yet, income growth does not seem to be the solution per se. Despite rapid economic growth in the past 20 years, South Asia, for example, continues to have inordinately high levels of undernourished children. This issue brings together a set of papers on trends, causes, and potential policy solutions related to undernutrition in South Asia.1 This region deserves special attention both because it accounts for the largest number of malnourished children in the world and because the rates of underweight and stunted children are puzzlingly high—higher than one would predict based on the region’s income or performance on other health indicators such as infant mortality. To give one example, if we use demographic and health surveys from the past 10 years to compare India and Sub-Saharan Africa, we see the incidence of underweight children is roughly twice as high in India, even though its population is significantly richer. In focusing on such anomalies, we believe this issue will present evidence and draw conclusions with applicability to developing countries in regions beyond South Asia.
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.
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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.126 | 0.051 |
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".