Found 1747 Hypotheses across 175 Pages (0.007 seconds)
  1. Various predictors of responses to malfeasance are associated with the responses of classic blood feuds, formal adjudication, individual self-redress (71)Ericksen, Karen Paige - "Blood feuds": cross-cultural variations in kin group vengeance, 1992 - 3 Variables

    Feuding and other responses to malfeasance are examined cross-culturally. The geographic distribution of responses are presented and odds ratios/logistic regression models are employed in analyzing the effect of socio-economic, historical and ecological variables on responses to malfeasance. Codes are included.

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  2. Resource base (hoe, plow, pastoral), diverging devolution and hospitable climate are associated with formal adjudication (73)Ericksen, Karen Paige - "Blood feuds": cross-cultural variations in kin group vengeance, 1992 - 4 Variables

    Feuding and other responses to malfeasance are examined cross-culturally. The geographic distribution of responses are presented and odds ratios/logistic regression models are employed in analyzing the effect of socio-economic, historical and ecological variables on responses to malfeasance. Codes are included.

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  3. Marriage mode, sanctions for premarital sex, and historical period are associated with blood feuds (73)Ericksen, Karen Paige - "Blood feuds": cross-cultural variations in kin group vengeance, 1992 - 4 Variables

    Feuding and other responses to malfeasance are examined cross-culturally. The geographic distribution of responses are presented and odds ratios/logistic regression models are employed in analyzing the effect of socio-economic, historical and ecological variables on responses to malfeasance. Codes are included.

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  4. The emphasis of a subsistence strategy on either land or aquatic animals will be associated with implement diversity.Read, Dwight - An Interaction Model for Resource Implement Complexity Based on Risk and Num..., 2008 - 6 Variables

    In this paper, the authors analyzed data on 20 hunter-gatherer groups in order to understand the factors that influence the diversity and elaborateness of their tool assemblages. They used data collected by a variety of ethnographers to draw inferences about the complexity of implement assemblages and how it is affected by ecological constraints, modes of resource procurement, group movement, and population size. Regression analysis showed that the two strongest predictors of implement complexity were growth season (GS) (as a proxy for risk) and the number of annual residential moves (NMV). With the understanding that NMV and GS are likely not independent, the authors created addition and interaction models to understand how these variables may work in tandem to influence implement diversity and elaborateness. The results show that a shorter growing season (higher risk) and a lower number of moves are correlated with greater implement complexity. This analysis also divided the hunter-gatherers into two subgroups: a subgroup characterized by higher diversity of complex implements and more elaborate individual implements than predicted by the model, and a subgroup characterized by lower diversity and less elaborateness than predicted. These subgroups were found to correspond with the distinction between foragers (groups that move more-or-less as a unit while gathering) and collectors (groups that gather (logistically from a more-or-less fixed settlement), with the higher diversity subgroup being made up mostly of collectors and the lower diversity subgroup being made up mostly of foragers. Finally, the authors suggest that under conditions where population growth leads to increased density, foraging strategies will tend to shift to collector strategies in conjunction with increased elaborateness of implements to exploit resources with greater intensity.

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  5. The emphasis of a subsistence strategy on either land or aquatic animals will be associated with implement elaborateness.Read, Dwight - An Interaction Model for Resource Implement Complexity Based on Risk and Num..., 2008 - 7 Variables

    In this paper, the authors analyzed data on 20 hunter-gatherer groups in order to understand the factors that influence the diversity and elaborateness of their tool assemblages. They used data collected by a variety of ethnographers to draw inferences about the complexity of implement assemblages and how it is affected by ecological constraints, modes of resource procurement, group movement, and population size. Regression analysis showed that the two strongest predictors of implement complexity were growth season (GS) (as a proxy for risk) and the number of annual residential moves (NMV). With the understanding that NMV and GS are likely not independent, the authors created addition and interaction models to understand how these variables may work in tandem to influence implement diversity and elaborateness. The results show that a shorter growing season (higher risk) and a lower number of moves are correlated with greater implement complexity. This analysis also divided the hunter-gatherers into two subgroups: a subgroup characterized by higher diversity of complex implements and more elaborate individual implements than predicted by the model, and a subgroup characterized by lower diversity and less elaborateness than predicted. These subgroups were found to correspond with the distinction between foragers (groups that move more-or-less as a unit while gathering) and collectors (groups that gather (logistically from a more-or-less fixed settlement), with the higher diversity subgroup being made up mostly of collectors and the lower diversity subgroup being made up mostly of foragers. Finally, the authors suggest that under conditions where population growth leads to increased density, foraging strategies will tend to shift to collector strategies in conjunction with increased elaborateness of implements to exploit resources with greater intensity.

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  6. The "collector" subgroup is associated with greater diversity than the "forager" subgroup.Read, Dwight - An Interaction Model for Resource Implement Complexity Based on Risk and Num..., 2008 - 3 Variables

    In this paper, the authors analyzed data on 20 hunter-gatherer groups in order to understand the factors that influence the diversity and elaborateness of their tool assemblages. They used data collected by a variety of ethnographers to draw inferences about the complexity of implement assemblages and how it is affected by ecological constraints, modes of resource procurement, group movement, and population size. Regression analysis showed that the two strongest predictors of implement complexity were growth season (GS) (as a proxy for risk) and the number of annual residential moves (NMV). With the understanding that NMV and GS are likely not independent, the authors created addition and interaction models to understand how these variables may work in tandem to influence implement diversity and elaborateness. The results show that a shorter growing season (higher risk) and a lower number of moves are correlated with greater implement complexity. This analysis also divided the hunter-gatherers into two subgroups: a subgroup characterized by higher diversity of complex implements and more elaborate individual implements than predicted by the model, and a subgroup characterized by lower diversity and less elaborateness than predicted. These subgroups were found to correspond with the distinction between foragers (groups that move more-or-less as a unit while gathering) and collectors (groups that gather (logistically from a more-or-less fixed settlement), with the higher diversity subgroup being made up mostly of collectors and the lower diversity subgroup being made up mostly of foragers. Finally, the authors suggest that under conditions where population growth leads to increased density, foraging strategies will tend to shift to collector strategies in conjunction with increased elaborateness of implements to exploit resources with greater intensity.

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  7. The frequency of hunter-gatherer movement is associated with implement diversity.Read, Dwight - An Interaction Model for Resource Implement Complexity Based on Risk and Num..., 2008 - 5 Variables

    In this paper, the authors analyzed data on 20 hunter-gatherer groups in order to understand the factors that influence the diversity and elaborateness of their tool assemblages. They used data collected by a variety of ethnographers to draw inferences about the complexity of implement assemblages and how it is affected by ecological constraints, modes of resource procurement, group movement, and population size. Regression analysis showed that the two strongest predictors of implement complexity were growth season (GS) (as a proxy for risk) and the number of annual residential moves (NMV). With the understanding that NMV and GS are likely not independent, the authors created addition and interaction models to understand how these variables may work in tandem to influence implement diversity and elaborateness. The results show that a shorter growing season (higher risk) and a lower number of moves are correlated with greater implement complexity. This analysis also divided the hunter-gatherers into two subgroups: a subgroup characterized by higher diversity of complex implements and more elaborate individual implements than predicted by the model, and a subgroup characterized by lower diversity and less elaborateness than predicted. These subgroups were found to correspond with the distinction between foragers (groups that move more-or-less as a unit while gathering) and collectors (groups that gather (logistically from a more-or-less fixed settlement), with the higher diversity subgroup being made up mostly of collectors and the lower diversity subgroup being made up mostly of foragers. Finally, the authors suggest that under conditions where population growth leads to increased density, foraging strategies will tend to shift to collector strategies in conjunction with increased elaborateness of implements to exploit resources with greater intensity.

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  8. The frequency of hunter-gatherer movement is associated with implement elaborateness.Read, Dwight - An Interaction Model for Resource Implement Complexity Based on Risk and Num..., 2008 - 6 Variables

    In this paper, the authors analyzed data on 20 hunter-gatherer groups in order to understand the factors that influence the diversity and elaborateness of their tool assemblages. They used data collected by a variety of ethnographers to draw inferences about the complexity of implement assemblages and how it is affected by ecological constraints, modes of resource procurement, group movement, and population size. Regression analysis showed that the two strongest predictors of implement complexity were growth season (GS) (as a proxy for risk) and the number of annual residential moves (NMV). With the understanding that NMV and GS are likely not independent, the authors created addition and interaction models to understand how these variables may work in tandem to influence implement diversity and elaborateness. The results show that a shorter growing season (higher risk) and a lower number of moves are correlated with greater implement complexity. This analysis also divided the hunter-gatherers into two subgroups: a subgroup characterized by higher diversity of complex implements and more elaborate individual implements than predicted by the model, and a subgroup characterized by lower diversity and less elaborateness than predicted. These subgroups were found to correspond with the distinction between foragers (groups that move more-or-less as a unit while gathering) and collectors (groups that gather (logistically from a more-or-less fixed settlement), with the higher diversity subgroup being made up mostly of collectors and the lower diversity subgroup being made up mostly of foragers. Finally, the authors suggest that under conditions where population growth leads to increased density, foraging strategies will tend to shift to collector strategies in conjunction with increased elaborateness of implements to exploit resources with greater intensity.

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  9. Risk in subsistence strategies is associated with implement diversity.Read, Dwight - An Interaction Model for Resource Implement Complexity Based on Risk and Num..., 2008 - 6 Variables

    In this paper, the authors analyzed data on 20 hunter-gatherer groups in order to understand the factors that influence the diversity and elaborateness of their tool assemblages. They used data collected by a variety of ethnographers to draw inferences about the complexity of implement assemblages and how it is affected by ecological constraints, modes of resource procurement, group movement, and population size. Regression analysis showed that the two strongest predictors of implement complexity were growth season (GS) (as a proxy for risk) and the number of annual residential moves (NMV). With the understanding that NMV and GS are likely not independent, the authors created addition and interaction models to understand how these variables may work in tandem to influence implement diversity and elaborateness. The results show that a shorter growing season (higher risk) and a lower number of moves are correlated with greater implement complexity. This analysis also divided the hunter-gatherers into two subgroups: a subgroup characterized by higher diversity of complex implements and more elaborate individual implements than predicted by the model, and a subgroup characterized by lower diversity and less elaborateness than predicted. These subgroups were found to correspond with the distinction between foragers (groups that move more-or-less as a unit while gathering) and collectors (groups that gather (logistically from a more-or-less fixed settlement), with the higher diversity subgroup being made up mostly of collectors and the lower diversity subgroup being made up mostly of foragers. Finally, the authors suggest that under conditions where population growth leads to increased density, foraging strategies will tend to shift to collector strategies in conjunction with increased elaborateness of implements to exploit resources with greater intensity.

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  10. Risk in subsistence strategies is associated with implement elaborateness.Read, Dwight - An Interaction Model for Resource Implement Complexity Based on Risk and Num..., 2008 - 7 Variables

    In this paper, the authors analyzed data on 20 hunter-gatherer groups in order to understand the factors that influence the diversity and elaborateness of their tool assemblages. They used data collected by a variety of ethnographers to draw inferences about the complexity of implement assemblages and how it is affected by ecological constraints, modes of resource procurement, group movement, and population size. Regression analysis showed that the two strongest predictors of implement complexity were growth season (GS) (as a proxy for risk) and the number of annual residential moves (NMV). With the understanding that NMV and GS are likely not independent, the authors created addition and interaction models to understand how these variables may work in tandem to influence implement diversity and elaborateness. The results show that a shorter growing season (higher risk) and a lower number of moves are correlated with greater implement complexity. This analysis also divided the hunter-gatherers into two subgroups: a subgroup characterized by higher diversity of complex implements and more elaborate individual implements than predicted by the model, and a subgroup characterized by lower diversity and less elaborateness than predicted. These subgroups were found to correspond with the distinction between foragers (groups that move more-or-less as a unit while gathering) and collectors (groups that gather (logistically from a more-or-less fixed settlement), with the higher diversity subgroup being made up mostly of collectors and the lower diversity subgroup being made up mostly of foragers. Finally, the authors suggest that under conditions where population growth leads to increased density, foraging strategies will tend to shift to collector strategies in conjunction with increased elaborateness of implements to exploit resources with greater intensity.

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