On the Adaptive Origins and Maladaptive Consequences of Human Inbreeding: Parasite Prevalence, Immune Functioning, and Consanguineous Marriage
Bibliographic record
Abstract
We propose that consanguineous marriages arise adaptively in response to high parasite prevalence and function to maintain coadapted gene complexes and associated local adaptation that defend against local pathogens. Therefore, a greater prevalence of inbreeding by consanguineous marriage is expected in geographical regions that historically have had high levels of disease-causing parasites. Eventually such marriages may, under the contemporary high movement of people with modern transportation, jeopardize the immunity of those who practice inbreeding as this leads to an increased susceptibility to novel pathogens. Therefore, a greater frequency of inbreeding is expected to predict higher levels of contemporary mortality and morbidity from infectious diseases. This parasite model of human inbreeding was supported by an analysis involving 72 countries worldwide. We found that historically high levels of pathogen prevalence were related positively to the proportion of consanguineous marriages, and that a higher prevalence of such marriages was associated with higher contemporary mortality and morbidity due to pathogens. Our study addresses plausible alternative explanations. The results suggest that consanguineous marriage is an adaptive consequence of historical pathogen ecologies, but is maladaptive in contemporary disease ecologies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".