Adverse environments: Investigating local variation in child growth
Bibliographic record
Abstract
Epigenetic and life history approaches to child growth are centered on the relationship between the organism and its environment. However, defining and operationalizing the concept of environment is challenging, in light of the multiple variables that influence growth. Moreover, the concept of adaptation as it applies to child growth is seldom considered in the developed country context. This paper presents a study of children living in three neighborhoods in the City of Hamilton, Ontario, Canada. Two of the communities are considered adverse environments on the basis of low socioeconomic status, and their inner city, industrial location. In contrast to children living in the higher socioeconomic status area, children in these adverse environments display negative growth indicators, i.e., somewhat constrained linear growth in one and risk for overweight and obesity in both. Although both these inner city neighborhoods constitute adverse environments, they differ in ways that have a significant impact on children's growth. We argue for a definition of "adverse environment" that is broadly based, incorporating a range of physical, social, and temporal factors that are highly localized and sensitive to community-level influences on growth and health. As well, we consider whether higher prevalence of overweight and obesity is adaptive in any way to these adverse environments and conclude that they are more likely to be deleterious than adaptive in either the long or short term.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".