Markus Bayer – författare

This book argues that democracies emerging from peaceful protest last longer, achieve higher levels of democratic quality, and are more likely to see at least two peaceful handovers of power than democracies that emerged out of violent resistance or top-down liberalization. Nonviolent resistance is not just an effective means of deposing dictators; it can also help consolidate democracy after the transition from autocratic rule. Drawing on case studies on democratic consolidation in Africa and Latin America, the authors find that nonviolent resistance creates a more inclusive transition process that is more resistant to democratic breakdown in the long term.

This book argues that democracies emerging from peaceful protest last longer, achieve higher levels of democratic quality, and are more likely to see at least two peaceful handovers of power than democracies that emerged out of violent resistance or top-down liberalization. Nonviolent resistance is not just an effective means of deposing dictators; it can also help consolidate democracy after the transition from autocratic rule. Drawing on case studies on democratic consolidation in Africa and Latin America, the authors find that nonviolent resistance creates a more inclusive transition process that is more resistant to democratic breakdown in the long term.

This book argues that democracies emerging from peaceful protest last longer, achieve higher levels of democratic quality, and are more likely to see at least two peaceful handovers of power than democracies that emerged out of violent resistance or top-down liberalization. Nonviolent resistance is not just an effective means of deposing dictators; it can also help consolidate democracy after the transition from autocratic rule. Drawing on case studies on democratic consolidation in Africa and Latin America, the authors find that nonviolent resistance creates a more inclusive transition process that is more resistant to democratic breakdown in the long term.

In diesem Buch wird argumentiert, dass Demokratien, die aus friedlichen Protesten hervorgegangen sind, länger bestehen, ein höheres Maß an demokratischer Qualität erreichen und mit größerer Wahrscheinlichkeit mindestens zwei friedliche Machtwechsel erleben als Demokratien, die aus gewaltsamem Widerstand oder einer Liberalisierung von oben hervorgegangen sind. Anhand von Fallstudien zur demokratischen Konsolidierung in Afrika und Lateinamerika kommt das Buch zu dem Ergebnis, dass gewaltfreier Widerstand einen integrativeren Übergangsprozess schafft, der langfristig widerstandsfähiger gegen den Zusammenbruch der Demokratie ist.

Auf Basis einer Mehrmethoden-Untersuchung aller empirischen Fälle in der Periode von 1960 bis 2007 entwickeln die Autoren dieses Bandes ein Modell der Ursachen von Staatskollaps. In 43 Fallstudien von Kollapsfällen und Vergleichsfällen werden verbreitete quantitative Indikatoren für fragile Staatlichkeit durch die Erhebung qualitativer Faktoren ergänzt. Der Vergleich mittels QCA ermöglicht dadurch erstmals die Überprüfung vieler gängiger Hypothesen. Durch die Ergänzung der QCA-Analysen mit Prozessanalysen können neben der Entwicklung des Kausalmodells neue Risikofaktoren identifiziert, bekannte Konzepte neu formuliert und Wirkungszusammenhänge zwischen Faktoren aufgedeckt werden. Auf diese Weise trägt das Buch zu einem besseren Verständnis von Staatskollaps und seinen Ursachen bei und erschließt bislang unberücksichtigte konzeptionelle und methodische Ansätze für die Forschung.

Auf Basis einer Mehrmethoden-Untersuchung aller empirischen Fälle in der Periode von 1960 bis 2007 entwickeln die Autoren dieses Bandes ein Modell der Ursachen von Staatskollaps. In 43 Fallstudien von Kollapsfällen und Vergleichsfällen werden verbreitete quantitative Indikatoren für fragile Staatlichkeit durch die Erhebung qualitativer Faktoren ergänzt. Der Vergleich mittels QCA ermöglicht dadurch erstmals die Überprüfung vieler gängiger Hypothesen. Durch die Ergänzung der QCA-Analysen mit Prozessanalysen können neben der Entwicklung des Kausalmodells neue Risikofaktoren identifiziert, bekannte Konzepte neu formuliert und Wirkungszusammenhänge zwischen Faktoren aufgedeckt werden. Auf diese Weise trägt das Buch zu einem besseren Verständnis von Staatskollaps und seinen Ursachen bei und erschließt bislang unberücksichtigte konzeptionelle und methodische Ansätze für die Forschung.

In today's fast-paced cybersecurity landscape, professionals are increasingly challenged by the vast volumes of cyber threat data, making it difficult to identify and mitigate threats effectively. Traditional clustering methods help in broadly categorizing threats but fall short when it comes to the fine-grained analysis necessary for precise threat management. Supervised machine learning offers a potential solution, but the rapidly changing nature of cyber threats renders static models ineffective and the creation of new models too labor-intensive. This book addresses these challenges by introducing innovative low-data regime methods that enhance the machine learning process with minimal labeled data. The proposed approach spans four key stages:Data Acquisition: Leveraging active learning with advanced models like GPT-4 to optimize data labeling.Preprocessing: Utilizing GPT-2 and GPT-3 for data augmentation to enrich and diversify datasets.Model Selection: Developing a specialized cybersecurity language model and using multi-level transfer learning.Prediction: Introducing a novel adversarial example generation method, grounded in explainable AI, to improve model accuracy and resilience.

In today's fast-paced cybersecurity landscape, professionals are increasingly challenged by the vast volumes of cyber threat data, making it difficult to identify and mitigate threats effectively. Traditional clustering methods help in broadly categorizing threats but fall short when it comes to the fine-grained analysis necessary for precise threat management. Supervised machine learning offers a potential solution, but the rapidly changing nature of cyber threats renders static models ineffective and the creation of new models too labor-intensive. This book addresses these challenges by introducing innovative low-data regime methods that enhance the machine learning process with minimal labeled data. The proposed approach spans four key stages:Data Acquisition: Leveraging active learning with advanced models like GPT-4 to optimize data labeling.Preprocessing: Utilizing GPT-2 and GPT-3 for data augmentation to enrich and diversify datasets.Model Selection: Developing a specialized cybersecurity language model and using multi-level transfer learning.Prediction: Introducing a novel adversarial example generation method, grounded in explainable AI, to improve model accuracy and resilience.

Traditionally, citizenship has been defined as the legal and political link between individuals and their democratic political community. However, traditional conceptions of democratic citizenship are currently challenged by various developments like migration, the rise of populism, increasing polarization, social fragmentation, and the challenging of representative democracy as well as developments in digital communication technology. Against this background, this peer reviewed book reflects recent conceptions of citizenship by bringing together insights from different disciplines, such as political science, sociology, economics, law, and history.