Youth at nutritional risk: malnourished or misnourished
Bibliographic record
Abstract
The authors thank Jon Gruber, Sara McLanahan, Don Kenkel and participants in the "Risky Behaviors " project for helpful comments. Currie is grateful for support from the Canadian Institute for Advanced Research, from NIH, and from NSF. Matthew Neidell provided excellent research We use data from the third National Health and Nutrition Examination Survey to examine the prevalence and determinants of poor nutritional outcomes among American youths. One strength of our analysis is that we focus on an array of nutritional outcomes, and we find in fact that the determinants of these outcomes vary considerably from outcome to outcome. We interpret our results using a model in which investments in health capital are affected by both resource constraints and a human capital production function that summarizes available nutrition information. We find that although many youths suffer from nutrient deficiencies (either anemia or vitamin deficiencies) these conditions are not generally sensitive to measures of resource constraints, and hence are unlikely to be due solely to a lack of food. Conversely, we find that our proxies for information matter. Our results suggest that broad-based policies designed to alter the composition of the diet may hold the greatest promise for addressing the nutritional problems of American youths.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".