Found 3613 Hypotheses across 362 Pages (0.004 seconds)
  1. Environmental productivity will be positively associated with land ownershipKavanaugh, Patrick - Drivers of global variation in land ownership, 2021 - 2 Variables

    Using multiple logistic regression, the researchers compare the relative strength of predictors of land ownership across 102 societies. The analysis finds significant predictive power in factors such as neighbors' property system, population density, and geography.

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  2. Population density will be positively associated with property ownershipKavanaugh, Patrick - Drivers of global variation in land ownership, 2021 - 2 Variables

    Using multiple logistic regression, the researchers compare the relative strength of predictors of land ownership across 102 societies. The analysis finds significant predictive power in factors such as neighbors' property system, population density, and geography.

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  3. Reliance on domestication will be positively associated with property ownershipKavanaugh, Patrick - Drivers of global variation in land ownership, 2021 - 2 Variables

    Using multiple logistic regression, the researchers compare the relative strength of predictors of land ownership across 102 societies. The analysis finds significant predictive power in factors such as neighbors' property system, population density, and geography.

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  4. Use of intensive agriculture will be positively associated with property ownershipKavanaugh, Patrick - Drivers of global variation in land ownership, 2021 - 2 Variables

    Using multiple logistic regression, the researchers compare the relative strength of predictors of land ownership across 102 societies. The analysis finds significant predictive power in factors such as neighbors' property system, population density, and geography.

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  5. Permanency of settlement patterns will be positively associated with land ownershipKavanaugh, Patrick - Drivers of global variation in land ownership, 2021 - 2 Variables

    Using multiple logistic regression, the researchers compare the relative strength of predictors of land ownership across 102 societies. The analysis finds significant predictive power in factors such as neighbors' property system, population density, and geography.

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  6. Coastal distance will be negatively associated with property ownership.Kavanaugh, Patrick - Drivers of global variation in land ownership, 2021 - 2 Variables

    Using multiple logistic regression, the researchers compare the relative strength of predictors of land ownership across 102 societies. The analysis finds significant predictive power in factors such as neighbors' property system, population density, and geography.

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  7. Unpredictability of resources will be negatively associated with property ownershipKavanaugh, Patrick - Drivers of global variation in land ownership, 2021 - 2 Variables

    Using multiple logistic regression, the researchers compare the relative strength of predictors of land ownership across 102 societies. The analysis finds significant predictive power in factors such as neighbors' property system, population density, and geography.

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  8. Nearest neighbor's property system will have a positive association with property ownershipKavanaugh, Patrick - Drivers of global variation in land ownership, 2021 - 2 Variables

    Using multiple logistic regression, the researchers compare the relative strength of predictors of land ownership across 102 societies. The analysis finds significant predictive power in factors such as neighbors' property system, population density, and geography.

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  9. Mountainous terrain will have a positive association with property ownershipKavanaugh, Patrick - Drivers of global variation in land ownership, 2021 - 2 Variables

    Using multiple logistic regression, the researchers compare the relative strength of predictors of land ownership across 102 societies. The analysis finds significant predictive power in factors such as neighbors' property system, population density, and geography.

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  10. Certain subsistence strategies will be associated with specific land ownership norms.Haynie, Hannah J. - The biogeography and evolution of land ownership, 2023 - 2 Variables

    This study asks the following research questions: What are the dynamics of land ownership norms over time? Do these changes follow predetermined trajectories? The authors utilize biogeographical and evolutionary analyses to explore temporal and spatial patterns in land ownership norms within a sample of 73 Bantu societies. For the first component of the research, they test three prominent hypotheses regarding evolutionary trajectories: rectilinear, unilinear, and shift without restriction. For the second component, they use a multi-model inference approach to evaluate three hypotheses regarding the possible spatial patterns of land ownership. The results show evidence for the unilinear trajectory in type, but not with a consistent decrease in group ownership in size. Land ownership is more likely in areas with neighboring landowners and predictable resource productivity, while subsistence type is not significantly correlated with type of land ownership.

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