Behav Brain Sci
September 2023
This commentary expands on Burt's concept of downward causation to include any association between genomic variants and a given outcome that is forged through social practices rather than biochemical pathways. It proposes the social stratification of population, through which endogamy over a period of generations produces allele frequency differences between socioeconomic strata, as a mechanism of downward causation.
View Article and Find Full Text PDFAs anthropogenic climate change threatens human existence on Earth, historians have begun to explore the scientific antecedents of environmental Malthusianism, the idea that human population growth is a major driver of ecosystem degradation and that environmental protection requires a reduction in human numbers. These accounts, however, neglect the antagonistic relationship between environmental Malthusianism and demography, thereby creating an illusion of scientific consensus. This article details the entwined histories of environmental Malthusianism and demography, revealing points of disagreement - initially over methods of analyzing and predicting population growth and later over the role of population growth in ecosystem degradation - and moments of strategic collaboration that benefited both groups of scientists.
View Article and Find Full Text PDFBig data is an exciting prospect for the field of economic history, which has long depended on the acquisition, keying, and cleaning of scarce numerical information about the past. This article examines two areas in which economic historians are already using big data - population and environment - discussing ways in which increased frequency of observation, denser samples, and smaller geographic units allow us to analyze the past with greater precision and often to track individuals, places, and phenomena across time. We also explore promising new sources of big data: organically created economic data, high resolution images, and textual corpora.
View Article and Find Full Text PDFThe Intermediate Data Structure (IDS) provides a standard format for storing and sharing individual-level longitudinal life-course data (Alter and Mandemakers 2014; Alter, Mandemakers and Gutmann 2009). Once the data are in the IDS format, a standard set of programs can be used to extract data for analysis, facilitating the analysis of data across multiple databases. Currently, life-course databases store information in a variety of formats, and the process of translating data into IDS can be long and tedious.
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