معرفی
Florian Schaffner is a postdoctoral researcher at the Department of Political Science at the University of Zurich, contributing to research and teaching in political science with expertise spanning political behavior, computational methods, and democratic processes.
His academic background includes:
- DPhil in Politics, University of Oxford, 2024
- MSc in Comparative Politics, London School of Economics and Political Science, 2018
- BA in Social Sciences, University of Zurich, 2016
Dr. Schaffner's research bridges political psychology with computational methods, focusing on affective polarization, representation, and direct democracy. His work integrates quantitative text analysis and machine learning to examine contemporary political phenomena, particularly how identities form and evolve around major events like referendums. This methodological innovation distinguishes his approach within political science.
His publications reveal a consistent trajectory examining democratic processes, with particular emphasis on Brexit's impact on political identities and party dynamics. The research demonstrates increasing sophistication in methodological approaches, moving from traditional political psychology to advanced computational techniques that capture nuanced voter behaviors and perceptions.
As an educator, Dr. Schaffner teaches courses including Natural Language Processing for Political Science and Quantitative Text Analysis at the postgraduate level, while also supervising Master's theses and providing research seminar preparation. His teaching reflects his dual expertise in political theory and computational methodology.
Dr. Schaffner has developed multiple R packages that advance political science methodology, including autoMrP for improving Multilevel Regression and Poststratification models, bodleianlibraries for library catalog access, websearchr for web operations, xaringanbeamer for academic presentations, and Xplorer for data exploration. These tools demonstrate his commitment to open science and methodological innovation in the discipline.
