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The Gene–Culture Interaction Framework and Implications for Health

2016· book-chapter· en· W2400807048 on OpenAlexaff
Joni Y. Sasaki, Jean Marie Leclair, Alexandria L. West, Heejung S. Kim

Bibliographic record

VenueOxford University Press eBooks · 2016
Typebook-chapter
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsYork University
FundersNeurosciences FoundationNational Science Foundation
KeywordsEpistemologyPsychologySocial psychologySociologyPhilosophy

Abstract

fetched live from OpenAlex

Abstract Based on the framework of gene–environment interactions (G  E), the gene–culture interaction framework demonstrates that a more complete understanding of thoughts and behaviors relevant to health may come from incorporating both genetic and cultural factors. Genes may interact with culture such that genetic predispositions lead to different outcomes depending on culture, and cultural differences on a given outcome may vary depending on genetic predispositions. We provide an overview of G  E research and some of the underlying biological mechanisms of these interactions. We explain the gene–culture interaction framework and discuss how culture is an important form of environment to consider that makes theoretical contributions unique from other forms of environment typically studied in G  E research. We discuss theoretical questions raised by gene–culture interaction research and specify how the gene–culture interaction framework can be applied to certain health issues.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.015
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.000

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 head0.087
GPT teacher head0.344
Teacher spread0.257 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreReview

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

Citations14
Published2016
Admission routes1
Has abstractyes

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