Bibliographic record
Abstract
This chapter focuses on the norms that lead to qualifying marriage as permitted versus “incestuous” (incorrect or prohibited) in the Ye’kwana marriage system. The Ye’kwana live in lowland South America/Amazonia, have a Dravidian kinship system, and a very high proportion of incestuous marriages. This chapter works with a rich data base; 200 marriages drawn from 2,366 people of whom 820 marriages were registered. The material collected by Silva Monterrey, together with that collected by de Barandiarán in the 1950s, shows that over a quarter of marriages were incestuous and that this pattern is old. Moreover, variations in the frequency of incestuous marriages cannot be explained as a consequence of demographic changes. One could not hope for a firmer demonstration of the importance of looking beyond the formal rules of marriage and exploring actual behaviour. Silva Monterrey throws down the challenge and asks, why do the Ye’kwana apparently commit marital incest with such astounding frequency?
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".