165 research outputs found

    Mortality in a Migrating Mennonite Church Congregation

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    Preston\u27s two-census method of demographic estimation is applied to three pairs of reconstructed censuses from the records of a migrating Mennonite church congregation covering the period 1780-1890, The three pairs of censuses correspond to three periods (1780-1790, 1850-1860, and 1880-1890) and to stays in three settings (Prussia, Russia, and Kansas, respectively). The Mennonites\u27 stay in Prussia was a period of hardship. In Russia they expanded their economic base and developed new farming methods, dramatically increasing their productivity. The Mennonites took these skills to Kansas, where they continued to be successful. The increase in life expectancy at age 5 corroborates this picture. The Prussian period exhibits the shortest life expectancy for both sexes. After the move to Russia, life expectancy increased for both sexes and continued to increase with the move to Kansas. The model also provides limited evidence for fertility depression following the move to Kansas

    Impact from Insourcing and Outsourcing on the Aerospace Industry of Oklahoma, a Mixed Method Study</

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    School of Teaching and Curriculum Leadershi

    Changes in Completed Family Size and Reproductive Span in Anabaptist Populations

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    The Anabaptist Amish, Hutterite and Mennonite peoples trace their origins to the Reformation. Although they share certain beliefs, such as adult baptism and the separation of church and state, each group is culturally unique. The Hutterite and Amish are highly fertile and their populations exhibit stable rates of growth. These demographic characteristics reflect communal living among the Hutterites and labor intensive farming practices among the Amish. The Mennonites are the most receptive Anabaptist group to outside socioeconomic influences and provide a demographic contrast to the more conservative Amish and Hutterites. Demographic data collected during a study of aging in Mennonite population samples from Goessel and Meridian, Kansas, 1980, and Henderson, Nebraska, 1981, formed the basis of a cohort analysis in order to assess fertility change over time. Completed family size has decreased significantly in all three communities since 1870. Since the early 1900\u27s the mean age of the mother at first birth has fluctuated but the mean age of mother at the birth of the last child is decreasing significantly for the communities of Goessel and Henderson, thus effectively shortening the reproductive span. The pattern is somewhat different for Meridian, the most conservative of the three communities

    Collective self-understanding: A linguistic style analysis of naturally occurring text data

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    Understanding what groups stand for is integral to a diverse array of social processes, ranging from understanding political conflicts to organisational behaviour to promoting public health behaviours. Traditionally, researchers rely on self-report methods such as interviews and surveys to assess groups’ collective self-understandings. Here, we demonstrate the value of using naturally occurring online textual data to map the similarities and differences between real-world groups’ collective self-understandings. We use machine learning algorithms to assess similarities between 15 diverse online groups’ linguistic style, and then use multidimensional scaling to map the groups in two-dimensonal space (N=1,779,098 Reddit comments). We then use agglomerative and k-means clustering techniques to assess how the 15 groups cluster, finding there are four behaviourally distinct group types – vocational, collective action (comprising political and ethnic/religious identities), relational and stigmatised groups, with stigmatised groups having a less distinctive behavioural profile than the other group types. Study 2 is a secondary data analysis where we find strong relationships between the coordinates of each group in multidimensional space and the groups’ values. In Study 3, we demonstrate how this approach can be used to track the development of groups’ collective self-understandings over time. Using transgender Reddit data (N= 1,095,620 comments) as a proof-of-concept, we track the gradual politicisation of the transgender group over the past decade. The automaticity of this methodology renders it advantageous for monitoring multiple online groups simultaneously. This approach has implications for both governmental agencies and social researchers more generally. Future research avenues and applications are discussed.</p

    Collective self-understanding: A linguistic style analysis of naturally occurring text data

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    Understanding what groups stand for is integral to a diverse array of social processes, ranging from understanding political conflicts to organisational behaviour to promoting public health behaviours. Traditionally, researchers rely on self-report methods such as interviews and surveys to assess groups' collective self-understandings. Here, we demonstrate the value of using naturally occurring online textual data to map the similarities and differences between real-world groups' collective self-understandings. We use machine learning algorithms to assess similarities between 15 diverse online groups' linguistic style, and then use multidimensional scaling to map the groups in two-dimensonal space (N=1,779,098 Reddit comments). We then use agglomerative and k-means clustering techniques to assess how the 15 groups cluster, finding there are four behaviourally distinct group types - vocational, collective action (comprising political and ethnic/religious identities), relational and stigmatised groups, with stigmatised groups having a less distinctive behavioural profile than the other group types. Study 2 is a secondary data analysis where we find strong relationships between the coordinates of each group in multidimensional space and the groups' values. In Study 3, we demonstrate how this approach can be used to track the development of groups' collective self-understandings over time. Using transgender Reddit data (N= 1,095,620 comments) as a proof-of-concept, we track the gradual politicisation of the transgender group over the past decade. The automaticity of this methodology renders it advantageous for monitoring multiple online groups simultaneously. This approach has implications for both governmental agencies and social researchers more generally. Future research avenues and applications are discussed

    Immunoglobulin Haplotypes – Markers of Reproductive Success

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    Immunoglobulin haplotypes are highly polymorphic and are useful for analyses of both macro- and microdifferentiation of populations. The origins of this diversity are not known, but recent reports suggest strong selection at this locus. Increased rates of first-trimester spontaneous abortions have been reported when parents share GM phenotypes. Reduced fertility has been observed in mixed European descent white and Hutterite populations when both parents share immunoglobulin haplotypes. Population samples with completed family information and GM haplotype data are rare; the objective here is to provide this information on another sample. A sample of 242 Mennonite couples with mothers older than 40 years was divided into 3 groups of matings based on how many haplotypes were shared: 0, 1, or 2. The distribution of mean completed family sizes for the three groups were 3.35 ± 1.85 ( n = 23), 3.47 ± 1.69 ( n = 128), and 3.37 ± 1.60 ( n = 91), respectively; these values were not significantly different (F = 0.145, p = 0.865). The log-rank test was used to compare the time-to-next-birth curves. The intervals between first and later births (2-4 births) were not significantly different for the three subgroups either. There is also only limited evidence for segregation distortion in another sample of 923 offspring (in which at least one parent is heterozygous)

    Spectral Templates from Multicolor Redshift Surveys

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    Understanding how the physical properties of galaxies (e.g. their spectral type or age) evolve as a function of redshift relies on having an accurate representation of galaxy spectral energy distributions. While it has been known for some time that galaxy spectra can be reconstructed from a handful of orthogonal basis templates, the underlying basis is poorly constrained. The limiting factor has been the lack of large samples of galaxies (covering a wide range in spectral type) with high signal-to-noise spectrophotometric observations. To alleviate this problem we introduce here a new technique for reconstructing galaxy spectral energy distributions directly from samples of galaxies with broadband photometric data and spectroscopic redshifts. Exploiting the statistical approach of the Karhunen-Loeve expansion, our iterative training procedure increasingly improves the eigenbasis, so that it provides better agreement with the photometry. We demonstrate the utility of this approach by applying these improved spectral energy distributions to the estimation of photometric redshifts for the HDF sample of galaxies. We find that in a small number of iterations the dispersion in the photometric redshifts estimator (a comparison between predicted and measured redshifts) can decrease by up to a factor of 2.Comment: 25 pages, 9 figures, LaTeX AASTeX, accepted for publication in A
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