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历史人口学中模型的演变

The Evolution of Models in Historical Demography.

作者信息

Alter George

机构信息

University of Michigan.

出版信息

J Interdiscip Hist. 2020 Winter;50(3):325-362. doi: 10.1162/jinh_a_01445. Epub 2019 Nov 1.

Abstract

This reflection on the evolution of methods and data in historical demography argues that we can still find inspiration and guidance in the work of the founders of our discipline. Historical demography is in the midst of a transition from a data-poor to a data-rich environment. Previous generations relied on demographic models to squeeze as much information as possible from the small amounts of data available. Today we live in a new era of large data sets and regression models. Researchers are creating both regional and international historical data sets of unprecedented size and depth. When examined closely, however, the methods that we use now make the same simplifying assumptions that generated the key advances of earlier generations. As we transition to new methods, demographic insight must inform our analyses and enrich our conclusions.

摘要

对历史人口学方法与数据演变的这一反思认为,我们仍能从本学科创始人的著作中获得灵感与指引。历史人口学正处于从数据匮乏环境向数据丰富环境的转变之中。前几代人依靠人口模型从少量可得数据中尽可能多地提取信息。如今我们生活在大数据集和回归模型的新时代。研究人员正在创建规模和深度前所未有的区域及国际历史数据集。然而,仔细审视后会发现,我们现在使用的方法做出了与早期几代人取得关键进展时相同的简化假设。在我们向新方法过渡时,人口学洞察力必须为我们的分析提供信息并丰富我们的结论。

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