This series of papers uses a large citation dataset compiled using big data tools (e.g., querying APIs) to examine gendered patterns of citations. Citations are important for career progress. However, who cites whom, who self cites, and who shares citations on social media is also shaped by implicit gender biases and practices in the academy. This project seeks to understand these processes in order to promote greater equity within the discipline of political science.
Impact
Peer-reviewed papers in international journals and international collaborations with the aim of improving awareness of gender biases and opportunities to remedy them.
Student Experience
Improved recognition of potential gender biases in our own reading lists and graduate curriculum.
