Factors influencing the allowance of cousin marriages in the standard cross cultural sample.
Bibliographic record
Abstract
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)
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".