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Political representation is a central pillar of modern democracies. Well-functioning representation is essential for trust between voters and political parties. However, the trust that political parties act in the interest of their voters is repeatedly challenged - not least by populist forces.

Human coding of large text corpora is an important, but very labor intensive and time-consuming pillar of political science research. Consequently, the rise and constant improvement of automated classification models presents itself as a perfect possibility to alleviate this burden, by enabling researchers to execute large coding tasks within a fraction of the time and work typically required. However, the reliability, consistency and quality of automated coding remains a concern. Can the computer really take over from humans and how comparable will the results be?

My dissertation examines how the regulatory environment of abortion and medically assisted reproduction (MAR) shapes the practices and experiences of multifetal pregnancy reduction (MFPR), a procedure to eliminate one or more fetal lives in multiple pregnancies. As the incidence of multiple pregnancy is significantly higher in MAR-induced pregnancies, MFPR is usually performed in the context of MAR, in which people intend to initiate pregnancy and achieve live birth; however, the procedure involves, at least partially, voluntary elimination of a fetal life.

The project "Visions of the Post-Viral Society: Changes in Political Perceptions in the Course of the Covid19-Crisis" investigates whether new forms and sources of political legitimacy emerged during and as a result of the Covid19 pandemic, in particular as a consequence of the strong political intervention in public life and the economy. Machine learning algorithms will be used to analyze the evolution of political discourse on social media before, during and after the crisis in order to identify new and changing political demands and interests of citizens active on these platforms.