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Record W2040053918 · doi:10.1002/ajhb.20664

Adverse environments: Investigating local variation in child growth

2007· article· en· W2040053918 on OpenAlexaffabout
Tina Moffat, Tracey Galloway

Bibliographic record

VenueAmerican Journal of Human Biology · 2007
Typearticle
Languageen
FieldMedicine
TopicBirth, Development, and Health
Canadian institutionsUniversity of Northern British ColumbiaMcMaster University
Fundersnot available
KeywordsSocioeconomic statusOperationalizationOverweightContext (archaeology)Environmental healthAdaptation (eye)Social environmentObesityGeographyDemographyPsychologyMedicineSociologyPopulation

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.310

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.292
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2007
Admission routes2
Has abstractyes

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