1.

Record Nr.

UNISA996354431403316

Autore

Stockinger Helena

Titolo

Dealing with religious difference in kindergarten : An ethnographic study in religiously affiliated institutions [[electronic resource]] / Helena Stockinger

Pubbl/distr/stampa

Münster, : Waxmann, 2019

2019, c2018

ISBN

3-8309-8983-0

Edizione

[1st, New ed.]

Descrizione fisica

1 online resource (258 p.)

Collana

Religious Diversity and Education in Europe ; 38

Soggetti

frühe Kindheit

Religion

Kita

Elementarbildung

interreligiös

religiöse Diversität

religious diversity

RE

Religious Education

Religionspädagogik

Sozialpädagogik und Pädagogik der frühen Kindheit

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Sommario/riassunto

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



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.

2.

Record Nr.

UNINA9910404090203321

Autore

Taylor Greg

Titolo

Claim Models: Granular Forms and Machine Learning Forms

Pubbl/distr/stampa

MDPI - Multidisciplinary Digital Publishing Institute, 2020

ISBN

3-03928-665-X

Descrizione fisica

1 online resource (108 p.)

Soggetti

Pharmaceutical chemistry and technology

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Sommario/riassunto

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



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.