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Die chinesische und westliche Forschung, die sich mit der Beziehung zwischen chinesischer Kultur und katholischer Kirche befasst, konzentriert sich in der Regel auf die katholische Kirche in China vor dem Verbot des Christentums. Die einzigartige Perspektive dieser Arbeit besteht darin, die Veränderungen in der Beziehung zwischen den beiden vom Ende der Ming-Dynastie bis zur ersten Hälfte des 20. Jahrhunderts zu untersuchen. Vor dem Verbot nährten die katholischen Missionare den konfuzianischen Gelehrten und verbanden die katholische Lehre mit dem Konfuzianismus, um ihren Einfluss in der Oberschicht der chinesischen Gesellschaft auszuüben. Nach dem Verbot achteten die katholischen Missionare nicht so sehr auf ihre Beziehung zur chinesischen Kultur wie ihre Vorgänger im 17. und 18. Jahrhundert. Einige Missionare sowie chinesische Katholiken wollten die Situation ändern und förderten gemeinsam die Gründung der Fu-Jen-Universität, die großen Wert auf die chinesische Kultur legte und die Beziehung zwischen der Katholischen Kirche und der chinesischen Kultur Anfang des 20. Jahrhunderts widerspiegeln konnte. Die Professoren der Abteilung Chinesisch und Geschichte leisteten den größten Beitrag zur Forschung der chinesischen Kultur an der Universität. Im Vergleich zu anderen wichtigen Universitäten in Peking, wo die chinesische Literatur im Fachbereich Chinesisch eine zentrale Stellung einnahm, legte die Fu-Jen-Universität mehr Wert auf die chinesische Sprache und Schriftzeichen. Anfang des 20. Jahrhunderts erlangten Frauen unter dem Einfluss der globalen feministischen Bewegung das Recht auf Hochschulbildung. Bis 1920 waren jedoch die katholischen Universitäten in Bezug auf die Hochschulbildung von Frauen Jahrzehnte hinter den protestantischen und nichtkirchlichen Universitäten zurückgefallen. Die Fu-Jen-Universität verbesserte diese Situation, indem sie nicht nur eine große Anzahl von Studentinnen annahm, sondern ihnen eine Vielzahl von Fächern einschließlich Chinesisch und Geschichte anbot. Im Allgemeinen konnte die Universität als Verbindung zwischen dem Katholizismus und der chinesischen Kultur in der ersten Hälfte des 20. Jahrhunderts angesehen werden. Sie spielte eine wichtige Rolle nicht nur bei der Erforschung und Verbreitung der chinesischen Kultur, sondern auch bei der Ausweitung des Einflusses der katholischen Kirche zu dieser Zeit.
There is no longer any doubt about the general effectiveness of psychotherapy. However, up to 40% of patients do not respond to treatment. Despite efforts to develop new treatments, overall effectiveness has not improved. Consequently, practice-oriented research has emerged to make research results more relevant to practitioners. Within this context, patient-focused research (PFR) focuses on the question of whether a particular treatment works for a specific patient. Finally, PFR gave rise to the precision mental health research movement that is trying to tailor treatments to individual patients by making data-driven and algorithm-based predictions. These predictions are intended to support therapists in their clinical decisions, such as the selection of treatment strategies and adaptation of treatment. The present work summarizes three studies that aim to generate different prediction models for treatment personalization that can be applied to practice. The goal of Study I was to develop a model for dropout prediction using data assessed prior to the first session (N = 2543). The usefulness of various machine learning (ML) algorithms and ensembles was assessed. The best model was an ensemble utilizing random forest and nearest neighbor modeling. It significantly outperformed generalized linear modeling, correctly identifying 63.4% of all cases and uncovering seven key predictors. The findings illustrated the potential of ML to enhance dropout predictions, but also highlighted that not all ML algorithms are equally suitable for this purpose. Study II utilized Study I’s findings to enhance the prediction of dropout rates. Data from the initial two sessions and observer ratings of therapist interventions and skills were employed to develop a model using an elastic net (EN) algorithm. The findings demonstrated that the model was significantly more effective at predicting dropout when using observer ratings with a Cohen’s d of up to .65 and more effective than the model in Study I, despite the smaller sample (N = 259). These results indicated that generating models could be improved by employing various data sources, which provide better foundations for model development. Finally, Study III generated a model to predict therapy outcome after a sudden gain (SG) in order to identify crucial predictors of the upward spiral. EN was used to generate the model using data from 794 cases that experienced a SG. A control group of the same size was also used to quantify and relativize the identified predictors by their general influence on therapy outcomes. The results indicated that there are seven key predictors that have varying effect sizes on therapy outcome, with Cohen's d ranging from 1.08 to 12.48. The findings suggested that a directive approach is more likely to lead to better outcomes after an SG, and that alliance ruptures can be effectively compensated for. However, these effects
were reversed in the control group. The results of the three studies are discussed regarding their usefulness to support clinical decision-making and their implications for the implementation of precision mental health.
The publication of statistical databases is subject to legal regulations, e.g. national statistical offices are only allowed to publish data if the data cannot be attributed to individuals. Achieving this privacy standard requires anonymizing the data prior to publication. However, data anonymization inevitably leads to a loss of information, which should be kept minimal. In this thesis, we analyze the anonymization method SAFE used in the German census in 2011 and we propose a novel integer programming-based anonymization method for nominal data.
In the first part of this thesis, we prove that a fundamental variant of the underlying SAFE optimization problem is NP-hard. This justifies the use of heuristic approaches for large data sets. In the second part, we propose a new anonymization method belonging to microaggregation methods, specifically designed for nominal data. This microaggregation method replaces rows in a microdata set with representative values to achieve k-anonymity, ensuring each data row is identical to at least k − 1 other rows. In addition to the overall dissimilarities of the data rows, the method accounts for errors in resulting frequency tables, which are of high interest for nominal data in practice. The method employs a typical two-step structure: initially partitioning the data set into clusters and subsequently replacing all cluster elements with representative values to achieve k-anonymity. For the partitioning step, we propose a column generation scheme followed by a heuristic to obtain an integer solution, which is based on the dual information. For the aggregation step, we present a mixed-integer problem formulation to find cluster representatives. To this end, we take errors in a subset of frequency tables into account. Furthermore, we show a reformulation of the problem to a minimum edge-weighted maximal clique problem in a multipartite graph, which allows for a different perspective on the problem. Moreover, we formulate a mixed-integer program, which combines the partitioning and the aggregation step and aims to minimize the sum of chi-squared errors in frequency tables.
Finally, an experimental study comparing the methods covered or developed in this work shows particularly strong results for the proposed method with respect to relative criteria, while SAFE shows its strength with respect to the maximum absolute error in frequency tables. We conclude that the inclusion of integer programming in the context of data anonymization is a promising direction to reduce the inevitable information loss inherent in anonymization, particularly for nominal data.
Building Fortress Europe Economic realism, China, and Europe’s investment screening mechanisms
(2023)
This thesis deals with the construction of investment screening mechanisms across the major economic powers in Europe and at the supranational level during the post-2015 period. The core puzzle at the heart of this research is how, in a traditional bastion of economic liberalism such as Europe, could a protectionist tool such as investment screening be erected in such a rapid manner. Within a few years, Europe went from a position of being highly welcoming towards foreign investment to increasingly implementing controls on it, with the focus on China. How are we to understand this shift in Europe? I posit that Europe’s increasingly protectionist shift on inward investment can be fruitfully understood using an economic realist approach, where the introduction of investment screening can be seen as part of a process of ‘balancing’ China’s economic rise and reasserting European competitiveness. China has moved from being the ‘workshop of the world’ to becoming an innovation-driven economy at the global technological frontier. As China has become more competitive, Europe, still a global economic leader, broadly situated at the technological frontier, has begun to sense a threat to its position, especially in the context of the fourth industrial revolution. A ‘balancing’ process has been set in motion, in which Europe seeks to halt and even reverse the narrowing competitiveness gap between it and China. The introduction of investment screening measures is part of this process.
In Luxemburg helfen externe Schulmediator*innen bei schulischen Konflikten. Die Anlaufstelle unterstützt bei drohenden Schulabbrüchen und Konflikten, die bei der Inklusion und Integration von Schüler*innen mit besonderem Förderbedarf oder mit Migrationshintergrund entstehen. Michèle Schilt sprach mit der Leiterin der Servicestelle, Lis De Pina, über die Arbeit der Schulmediation.
Emotionen gelten als Spiegelbild unserer persönlichen Bedürfnislage. Insbesondere in Konflikt- oder Mediationsgesprächen ist es demnach wichtig, nicht nur über den Moment zu sprechen, an dem ein Streit entstanden ist, sondern auch Bedürfnisse und Gefühle aufzudecken, die unser Handeln, Denken und Fühlen beeinflusst haben. Die folgenden Materialien zeigen, wie man als Lehrkraft Emotionen und Streit mit Grundschulkindern behandeln kann.
Sie haben eine spannende politische Diskussion in der Klasse. Das Gros Ihrer Schüler*innen ist wach, interessiert und engagiert. Alles läuft prima. Doch dann passiert's: Einer oder eine von ihnen stellt – absichtlich oder unreflektiert – eine extremistische oder verschwörungstheoretische Aussage in den Raum. Und nun?