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Record W2114322854 · doi:10.1037/ebs0000034

Factors influencing the allowance of cousin marriages in the standard cross cultural sample.

2015· article· en· W2114322854 on OpenAlexaff
Ashley D. Hoben, Abraham P. Buunk, Maryanne L. Fisher

Bibliographic record

VenueEvolutionary Behavioral Sciences · 2015
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsSaint Mary's University
FundersUniversity of Illinois at Urbana-ChampaignUniversity of Cambridge
KeywordsCousinSocial psychologyVariance (accounting)PsychologyPreferenceDevelopmental psychologyAllowance (engineering)PsycINFOSociologyDemographyLawPolitical science

Abstract

fetched live from OpenAlex

The purpose of this study is to examine variance in the practice and acceptance of cousin marriage in select areas of the world. This study uses Murdock’s Standard Cross Cultural Sample (SCCS). The SCCS includes 186 societies ranging from contemporary hunter and gatherers to early historic states to contemporary industrial people. It is hypothesized that cousin marriages are more likely to occur in small, isolated communities, and in communities that experience high rates of pathogen prevalence. That is, the variance in the practice of cousin marriage may reflect functional responses to various local ecological and environmental pressures. The results demonstrate that geographic isolation and pathogen prevalence are both independent and significant positive predictors of whether or not a society practices cousin marriage. These findings suggest that consanguineous marriage may be an adaptive solution to the problem of mate selection, depending on the environment in which one lives. Consequently, the biological advantages may lead to and/or become an individual preference, which is then reinforced by the local culture. We contend that although social and cultural explanations are of obvious importance, they can only provide partial explanations, and much can be gained from incorporating an evolutionary perspective. (PsycINFO Database Record (c) 2016 APA, all rights reserved)<br/><br/>

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 score0.031
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.327
GPT teacher head0.473
Teacher spread0.147 · 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

Citations8
Published2015
Admission routes1
Has abstractyes

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