GeographielehrerInnen erproben Service Learning. Empirische Rekonstruktion von Bewertungsmustern, Akzeptanzkomponenten und Gelingensbedingungen für einen gemeinwohlorientierten Geographieunterricht

Ausgangspunkt dieser Arbeit ist die Frage: „Wovon hängt die Bewertung von Service Learning-Projekten durch GeographielehrerInnen ab?“. Der empirischen Beantwortung dieser Forschungsfrage geht eine theoretische und literaturgestütze Potenzialanalyse von „Service Learning im Geographieunterricht“ voraus. Diese offenbart, dass Service Learning als innovativer und vielversprechender konzeptioneller Ansatz für einen an den Bildungsstandards (DGFG 2012) orientierten und modernen Geographieunterricht bewertet werden kann. Sie zeigt jedoch auch, dass Service Learning - trotz dieser vermeintlichen Potenzialvielfalt - bislang nahezu keine Anwendung im Geographieun-terricht gefunden hat. Es ist das Ziel dieser Arbeit zu erforschen, welche Gründe und Barrieren für den zu-rückhaltenden Umgang der GeographielehrerInnen mit der Unterrichtskonzeption vor-liegen und - in einer positiven Betrachtungsweise und als Forschungsschwerpunkt - welche Akzeptanzkomponenten für Service Learning im Geographieunterricht sich aus den Wahrnehmungs- und Bewertungsmustern von LehrerInnen, die erstmalig ein Ser-vice Learning-Projekt umgesetzt haben, ableiten lassen. Diese so gewonnenen Akzep-tanzkomponenten werden abschließend im Sinne der Grounded Theory (GLASER & STRAUSS 1967) zu einem Gelingensbedingungsgefüge verknüpft. Dieses kann als wis-senschaftlich hergeleitete Hilfestellung für Initiations- und Implementationsprozesse der Unterrichtskonzeption Service Learning in den Geographieunterricht verstanden werden und richtet sich somit an GeographielehrerInnen, FachleiterInnen und Geographiedidak-tikerInnen.

Micro-Foundations of Financialization. Status Anxiety and the Market for Consumer Credit in Chile

This project investigates why people in Chile acquired so much consumer debt in contexts of material prosperity, and asks what the role of inequality and commodification is in this process. The case raises an important challenge to the literature. Insofar as existing accounts assume that the financialization of consumption occurs in contexts marked by wage stagnation and a general deterioration of the middle classes, they engender two contradictory explanations: while political economists argue that people use credit in order to smooth their consumption in the face of market volatility, economists maintain that concentration of wealth at the top pushes middle income consumers to emulate the expenditures of the rich and consume beyond their means. These explanations do not necessarily fit the reality of developing countries. Triangulating in-depth interviews with middle class families, multivariate statistical analysis and secondary literature, the project shows that consumers in Chile use credit to finance “ordinary” forms of consumption that do not aim either at coping with market instability or emulating and signaling status to others. Rather, Chileans use department store credit cards in order to acquire a standard package of “inconspicuous” goods that they feel entitled to have. From this point of view, the systematic indebtedness of consumers originates in a major concern with “rank”, “achievement” and "security" that – following De Botton -- I call “status anxiety”. Status anxiety does not stem from the desire to emulate rich consumers, but from the impossibility of complying with normative expectations about what a middle class family should be (and have) that outweigh wage improvements. The project thus investigates the way in which “status anxiety” is systematically reproduced by means of two broad mechanisms that prompt people to acquire consumer debt. The first mechanism generating debt stems from an increase of real wages and high levels of inequality. It is explained by a general sociological principle known as relative deprivation, which points to the fact that general satisfaction with one´s income, possessions or status, is assessed not in absolute terms such as total income, but in relation with reference groups. In this sense, I explore the mechanisms that operate as catalyzers of relative deprivation, by making explicit social inequalities and distorting the perception of others´ wealth. Despite upward mobility and economic improvement, Chileans share the perception of “falling behind,” which materializes in an “imaginary middle class” against which people compare their status, possessions and economic independence. Finally, I show that the commodification of education, health and pension funds does not directly prompt people to acquire consumer debt, but operate as “income draining” mechanisms that demand higher shares of middle class families’ “discretionary income.” In combination with “relative deprivation,” these “income draining” mechanisms leave families with few options to perform their desired class identities, other than learning how to bring resources from the future into the present with the help of department store credit cards.

Geometrical Methods in Multivariate Risk Management: Algorithms and Applications

This thesis builds a framework for evaluating downside risk from multivariate data via a special class of risk measures (RM). The peculiarity of the analysis lies in getting rid of strong data distributional assumptions and in orientation towards the most critical data in risk management: those with asymmetries and heavy tails. At the same time, under typical assumptions, such as the ellipticity of the data probability distribution, the conformity with classical methods is shown. The constructed class of RM is a multivariate generalization of the coherent distortion RM, which possess valuable properties for a risk manager. The design of the framework is twofold. The first part contains new computational geometry methods for the high-dimensional data. The developed algorithms demonstrate computability of geometrical concepts used for constructing the RM. These concepts bring visuality and simplify interpretation of the RM. The second part develops models for applying the framework to actual problems. The spectrum of applications varies from robust portfolio selection up to broader spheres, such as stochastic conic optimization with risk constraints or supervised machine learning.