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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 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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.961
Threshold uncertainty score0.465

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.087
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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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