The authors examine correlations between slavery and variables that can potentially be detected archaeologically. The authors do not test specific hypotheses, but aim to explore the variables in a broader sense. As such, the authors use a grounded theory approach to data analysis in order to examine trends that emerge from the data itself.

The authors examine correlations between slavery and variables that can potentially be detected archaeologically. The authors do not test specific hypotheses, but aim to explore the variables in a broader sense. As such, the authors use a grounded theory approach to data analysis in order to examine trends that emerge from the data itself.

The authors examine correlations between slavery and variables that can potentially be detected archaeologically. The authors do not test specific hypotheses, but aim to explore the variables in a broader sense. As such, the authors use a grounded theory approach to data analysis in order to examine trends that emerge from the data itself.

The authors examine correlations between slavery and variables that can potentially be detected archaeologically. The authors do not test specific hypotheses, but aim to explore the variables in a broader sense. As such, the authors use a grounded theory approach to data analysis in order to examine trends that emerge from the data itself.

The authors examine correlations between slavery and variables that can potentially be detected archaeologically. The authors do not test specific hypotheses, but aim to explore the variables in a broader sense. As such, the authors use a grounded theory approach to data analysis in order to examine trends that emerge from the data itself.

The authors examine correlations between slavery and variables that can potentially be detected archaeologically. The authors do not test specific hypotheses, but aim to explore the variables in a broader sense. As such, the authors use a grounded theory approach to data analysis in order to examine trends that emerge from the data itself.

This chapter explores and tests some propositions about matrilineal societies. Supplementary to that discussion, the author also explores the problems of method associated with the use of coded data on large samples of cultures.

The authors examine correlations between slavery and variables that can potentially be detected archaeologically. The authors do not test specific hypotheses, but aim to explore the variables in a broader sense. As such, the authors use a grounded theory approach to data analysis in order to examine trends that emerge from the data itself.

The authors use ethnographic data to try to shed light on the prevalence of missing phalanges in Upper Paleolithic cave images. Searching eHRAF World Cultures, they found evidence of finger amputation in 121 societies. These accounts cast doubt on two common theories: 1) that cave images reflect sign language or 2) counting systems. Researchers argue the intentional removal of fingers could be sorted into the 10 following categories: sacrifice (for deities), mourning (for grief), identity (for group membership), medical (to heal sickness), marriage (status marker), punishment (for deeds), veneration (for worship), offering (post mortem for deities), trophy (an enemies fingers), and talisman (assist with magic). They argue that sacrifice was the most likely reason for the missing finger images in Upper Paleolithic caves.

Researchers examine marriage patterns of 186 societies from the Standard Cross-Cultural Sample (SCCS). The eleven predictor variables are pathogen stress, arranged female marriages, population density, father roles during infancy, temperature, social stratification, wealth inequality, internal warfare, assault frequency, female agricultural contribution, and sex ratio. The two outcome variables measuring polygyny are cultural rules constraining polygyny and the percentage of married men who are polygynous. Controlling on phylogeny using a global supertree of the languages, analysis of marriage patterns reveals that assault frequency and pathogen stress are the strongest predictors of polygyny. These findings offer additional support for the theories of harem-defense polygyny and male genetic quality.