Machine Learning in Political Research

Political Methodology

Quantitative

Artificial Intelligence

Code:

P335

Chair(s)

Theodore Chadjipadelis

Aristotle University of Thessaloniki

Co-Chair(s)

Iris Reus

Johann Wolfgang Goethe-Universität Frankfurt

Section Methods in Motion: Adapting Political Research Methods to Evolving Realities

This panel brings together papers focusing on the application of machine learning approaches to political research

Title	Details
Close Calls of Conflict: How False Positives Can Improve Conflict Onset Prediction	View Paper Details
Applying Machine Learning to Research on Historical Archives	View Paper Details
Expert- And LLM-Generated Data Inputs for the Prediction of Public Policies	View Paper Details
A New Paradigm for Supervised Machine Learning in Political Science: Tabular Foundation Models	View Paper Details