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Record W3176581403 · doi:10.1016/j.ssmph.2021.100849

The shifting income-obesity relationship: Conditioning effects from economic development and globalization

2021· article· en· W3176581403 on OpenAlexafffund
Min Zhou

Bibliographic record

VenueSSM - Population Health · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsUniversity of Victoria
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Victoria
KeywordsGlobalizationObesityEconomic inequalityIncome distributionEconomic globalizationEconomicsDistribution (mathematics)Development economicsDemographic economicsEconomic growthInequalityMedicineMarket economyEndocrinology

Abstract

fetched live from OpenAlex

The literature has long been debating whether it is high-income or low-income individuals who face higher risks of obesity. In this study I contend that this mixed record about the income-obesity relationship is the result of a failure to account fully for macro-level social contexts. The income-obesity relationship is not uniform in all societies but is conditioned by macro-level social contexts including the society's economic development and involvement in globalization. The 2011 Module on Health and Health Care of the International Social Survey Programme (ISSP) provides an ideal opportunity for testing the complex income-obesity relationship in a cross-country setting. Employing multilevel models with cross-level interactions, this study finds that the shift in the effect of income from obesity-promoting to obesity-depressing is facilitated by both economic development and globalization. Under the combined forces of economic development and globalization, obesity increasingly becomes a burden of the poor in a society and the social distribution of obesity increasingly mirrors existing social inequality. Nevertheless, the economic development and globalization thresholds for shifting into a significant obesity-depressing effect of income are high.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.253
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
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 head0.024
GPT teacher head0.302
Teacher spread0.278 · 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.

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

Citations21
Published2021
Admission routes2
Has abstractyes

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