Project Lead: Bree Bang-Jensen, Department of Political Science
eScience Liaison: Jose Hernandez and Spencer Wood
The greatest challenges of the 21st century are cross-national, including climate change, migration, epidemics, inequality and financial corruption. As a result, it is critical that we better understand the factors that endanger international cooperation. Despite a wealth of research on how the design of international treaties affects treaty commitment and compliance, we only have snapshots of how delegation to third parties, enforcement, and precision differ across treaties. Because most treaties are publicly available text documents, this research area provides a veritable goldmine for the application of cutting edge NLP/machine learning tools—trained on highly curated datasets—to the messy, real world data of most interest and value to addressing pressing social science questions.
We will work to identify the frequency of these different elements of treaty design and legalization with the help of a stratified sample of 2,000 human labeled treaties. We might use these human labels to create a supervised machine learning model that then can predict labels for the universe of 55,000 treaties. Alternately, we may use natural language preprocessing strategies to handle the idiosyncrasies of these data. This project will help future treaty negotiators better understand the features of legalization that improve treaty durability and compliance and thus draft treaties that better contribute to cooperative outcomes, and second, detailed data on treaty design will enable other researchers in political science, sociology, economics and international law to research questions and test hypotheses that are currently not possible to explore due to limited data.