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1. |
Record Nr. |
UNISA996354431403316 |
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Autore |
Stockinger Helena |
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Titolo |
Dealing with religious difference in kindergarten : An ethnographic study in religiously affiliated institutions [[electronic resource]] / Helena Stockinger |
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Pubbl/distr/stampa |
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Münster, : Waxmann, 2019 |
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2019, c2018 |
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ISBN |
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Edizione |
[1st, New ed.] |
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Descrizione fisica |
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1 online resource (258 p.) |
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Collana |
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Religious Diversity and Education in Europe ; 38 |
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Soggetti |
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frühe Kindheit |
Religion |
Kita |
Elementarbildung |
interreligiös |
religiöse Diversität |
religious diversity |
RE |
Religious Education |
Religionspädagogik |
Sozialpädagogik und Pädagogik der frühen Kindheit |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Sommario/riassunto |
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In kindergartens children of different religions and with different religious attitudes meet, play and learn together. How do kindergartens deal with religious differences and how do children address this topic? The ethnographic study reported in this book explores this question in two kindergartens, one run by a Catholic, the other by an Islamic organization. The results illustrate how important it is to handle religious difference attentively. In order to reduce structural discrimination, the author considers how a culture of recognition can be developed, with religious difference being given the attention it |
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requires. This book provides a valuable contribution to a difference-sensitive interaction in educational institutions. |
This insightful study is a delight to read as it opens up for us the voices of very young children about their experience and understanding of religious difference. - Sandra Cullen, in: British Journal of Religious Education 42:1, pp. 106-108. |
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2. |
Record Nr. |
UNINA9910404090203321 |
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Autore |
Taylor Greg |
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Titolo |
Claim Models: Granular Forms and Machine Learning Forms |
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Pubbl/distr/stampa |
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MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
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ISBN |
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Descrizione fisica |
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1 online resource (108 p.) |
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Soggetti |
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Pharmaceutical chemistry and technology |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Sommario/riassunto |
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This collection of articles addresses the most modern forms of loss reserving methodology: granular models and machine learning models. New methodologies come with questions about their applicability. These questions are discussed in one article, which focuses on the relative merits of granular and machine learning models. Others illustrate applications with real-world data. The examples include neural networks, which, though well known in some disciplines, have previously been limited in the actuarial literature. This volume expands on that literature, with specific attention to their application to loss reserving. For example, one of the articles introduces the application of neural networks of the gated recurrent unit form to the actuarial literature, whereas another uses a penalized neural network. Neural networks are not the only form of machine learning, and two other papers outline applications of gradient boosting and regression trees respectively. Both articles construct loss reserves at the individual claim level so that these models resemble granular models. One of these |
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articles provides a practical application of the model to claim watching, the action of monitoring claim development and anticipating major features. Such watching can be used as an early warning system or for other administrative purposes. Overall, this volume is an extremely useful addition to the libraries of those working at the loss reserving frontier. |
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