12,808 research outputs found

    Religious Identity Formation Among Adolescents: The Role of Religious Secondary Schools

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    The purpose of this article is to examine the role religious secondary schools play in the religious identity formation of adolescents. Although several research studies have found a correlation between enrollment in private religious schools and adolescents’ religious identity formation, the researchers of these studies have only speculated about which specific characteristics of religious schools are responsible for this formation in the lives of adolescents. Through a review of the literature, the present article identifies several characteristics of religious secondary schools that may contribute to the process of religious identity formation: a community of religious peers, the presence of religious adults, and an exposure to religious instruction. Implications for Christian secondary school practitioners are also discussed

    Public Capital Spillovers and Growth: A Foray Downunder

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    We extend the deterministic growth model of Glomm and Ravikumar (1994) to a stochastic endogenous growth model which nests both exogenous and endogenous growth factors. By introducing simple shocks to production technology, private capital and public capital investment, we can derive testable time series properties of the analytical model. The hypothesis of strict endogenous growth due to public capital spillovers cannot be statistically rejected for our Australian data set. We find further short-run evidence of public capital contributing to permanent increases in the levels of per capita income and private capital.

    Statistical methods for tissue array images - algorithmic scoring and co-training

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    Recent advances in tissue microarray technology have allowed immunohistochemistry to become a powerful medium-to-high throughput analysis tool, particularly for the validation of diagnostic and prognostic biomarkers. However, as study size grows, the manual evaluation of these assays becomes a prohibitive limitation; it vastly reduces throughput and greatly increases variability and expense. We propose an algorithm - Tissue Array Co-Occurrence Matrix Analysis (TACOMA) - for quantifying cellular phenotypes based on textural regularity summarized by local inter-pixel relationships. The algorithm can be easily trained for any staining pattern, is absent of sensitive tuning parameters and has the ability to report salient pixels in an image that contribute to its score. Pathologists' input via informative training patches is an important aspect of the algorithm that allows the training for any specific marker or cell type. With co-training, the error rate of TACOMA can be reduced substantially for a very small training sample (e.g., with size 30). We give theoretical insights into the success of co-training via thinning of the feature set in a high-dimensional setting when there is "sufficient" redundancy among the features. TACOMA is flexible, transparent and provides a scoring process that can be evaluated with clarity and confidence. In a study based on an estrogen receptor (ER) marker, we show that TACOMA is comparable to, or outperforms, pathologists' performance in terms of accuracy and repeatability.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS543 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org
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