Child stunting is associated with weaker human capital among native Amazonians
Bibliographic record
Abstract
OBJECTIVES: We assessed associations between child stunting, recovery, and faltering with schooling and human capital skills in a native Amazonian society of horticulturalists-foragers (Tsimane'). METHODS: We used cross-sectional data (2008) from 1262 children aged 6 to 16 years in 53 villages to assess contemporaneous associations between three height categories: stunted (height-for-age Z score, HAZ<-2), moderately stunted (-2 ≤ HAZ≤-1), and nonstunted (HAZ>-1), and three categories of human capital: completed grades of schooling, test-based academic skills (math, reading, writing), and local plant knowledge. We used annual longitudinal data (2002-2010) from all children (n = 853) in 13 villages to estimate the association between changes in height categories between the first and last years of measure and schooling and academic skills. RESULTS: Stunting was associated with 0.4 fewer completed grades of schooling (∼24% less) and with 13-15% lower probability of showing any writing or math skills. Moderate stunting was associated with ∼20% lower scores in local plant knowledge and 9% lower probability of showing writing skills, but was not associated with schooling or math and writing skills. Compared with nonstunted children, children who became stunted had 18-21% and 15-21% lower probabilities of showing math and writing skills, and stunted children had 0.4 fewer completed grades of schooling. Stunted children who recovered showed human capital outcomes that were indistinguishable from nonstunted children. CONCLUSIONS: The results confirm adverse associations between child stunting and human capital skills. Predictors of growth recovery and faltering can affect human capital outcomes, even in a remote, economically self-sufficient society.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".