Edward Schillebeeckx (1914-2009) is a key figure in modern theology. As one of the editors of Concilium (with Karl Rahner) he was the advisor of the Dutch bishops at the Second Vatican Council and has been said to have influenced much of the content of the Council's documents. Later he had to defend his theology before the Magisterium as his orthodoxy was doubted. As a theologian, he always sought to balance between tradition and renewal. The cultural and political situation of his time played an important part in the development of his theological ideas. He connected developments in science and culture with his theology and with the life of people in the Church. This introduction guides the reader through some of Schillebeeckx's key ideas. Stephan van Erp shows how Schillebeeckx linked history and tradition to new experiences and to the spirit of his own time and how, in doing so, Schillebeeckx innovated our understanding of Christ, faith and the Church.
This Element argues that to understand why transparency “works” in one context, but fails in another, we have to take into account how institutional (macro), organizational (meso) contexts interact with individual behavior (micro). A review of research from each of these perspectives shows that the big promises thought to accompany greater transparency during the first two decades of the 20th century have not been delivered. For example, transparency does not necessarily lead to better government performance and more trust in government. At the same time, transparency is still a hallmark of democratic governance and as this book highlights, for instance, transparency has been relatively successful in combating government corruption. Finally, by explicitly taking a multilayered perspective into account, this Element develops new paths for future research.
Computational modeling is now ubiquitous in psychology, and researchers who are not modelers may find it increasingly difficult to follow the theoretical developments in their field. This book presents an integrated framework for the development and application of models in psychology and related disciplines. Researchers and students are given the knowledge and tools to interpret models published in their area, as well as to develop, fit, and test their own models. Both the development of models and key features of any model are covered, as are the applications of models in a variety of domains across the behavioural sciences. A number of chapters are devoted to fitting models using maximum likelihood and Bayesian estimation, including fitting hierarchical and mixture models. Model comparison is described as a core philosophy of scientific inference, and the use of models to understand theories and advance scientific discourse is explained.
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