Understanding the gaps and connections across existing theories and findings is a perennial challenge in scientific research. To encourage systematic, replicable, and transparent methods for assessing literature, we propose an accessible network-based framework for reviewing scholarship. We demonstrate how network statistics and visualization allow researchers to see patterns and offer reproducible characterizations of assertions about the major themes in existing literature with an example on the redistricting literature. 

Adeline Lo is an Assistant Professor at the Department of Political Science at the University of Wisconsin-Madison. She researches factors that motivate or mitigate conflict between groups, and focuses on migrant inclusionary politics; Lo also designs statistical tools for inference, prediction and measurement for applied social science data. Her work has been published or is forthcoming in journals such as the American Political Science Review, Political Analysis and Proceedings of the National Academy of Sciences.