Historical life course studies最新文献

The North Orkney Population History Project is a multidisciplinary data collection, digitization, and analysis effort that aims to reconstruct longitudinal demographic, environmental, and economic change. We describe the motivation, methodological approach, data sources, and some initial findings of the project. Detailed contextual information about a single community allows for the joint analysis of the changing population and changing landscape. The combination of diverse data sources and disciplinary approaches has resulted in findings that would not have been possible if each source had been considered in isolation. The approach adopted by the project offers a way to examine the interaction of a population with its landscape over a period of change.

This paper provides an examination into some of the most enduring debates regarding tuberculosis mortality during the nineteenth century: those related to gender, geographic and temporal variations. We use populations reconstructed from individual census and civil register data for the period 1861 to 1901, comparing a growing urban area with a declining rural area, both with around 20,000 inhabitants in 1861. Our analysis shows that among young adults tuberculosis was linked to excess female mortality in the urban area and excess male mortality in the rural area. We demonstrate that in the town textile workers of both genders had particularly high mortality from tuberculosis, and that the only reason for higher overall female mortality was the predominance of young women in the textile labour force. We show that the age and gender-specific pattern of mortality in the rural area is consistent with higher male than female out-migration together with return migration of those who had contracted the disease elsewhere and needed care during their lengthy illness. We argue that the observed patterns are difficult to reconcile with the 'bargaining-nutrition' account of gendered patterns in tuberculosis mortality, and that they provide little support for nutrition as a key influence on the disease. However, our findings do reinforce Andrew Hinde's recent argument that geographical patterns in sex-specific tuberculosis mortality rates were largely determined by migration patterns, and we discuss the implications of this for our understanding of the decline of the disease over the late nineteenth century.

The 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. The IDS Transposer is a software tool that automates this process for source data in any format, allowing database administrators to specify how their datasets are to be represented in IDS. This article describes how the IDS Transposer works, first by going through an example step-by-step, and then by discussing each part of the process and potential options and exceptions in detail.

The Intermediate Data Structure (IDS) is a standard data format that has been adopted by several large longitudinal databases on historical populations. Since the publication of the first version in Historical Social Research in 2009, two improved and extended versions have been published in the Collaboratory Historical Life Courses. In this publication we present version 4 which is the latest 'official' standard of the IDS. Discussions with users over the last four years resulted in important changes, like the inclusion of a new table defining the hierarchical relationships among 'contexts,' decision schemes for recording relationships, additional fields in the metadata table, rules for handling stillbirths, a reciprocal model for relationships, guidance for linking IDS data with geospatial information, and the introduction of an extended IDS for computed variables.