Computing Gender
Year: 2025
Type: preprint
Abstract: Studying gender presents unique challenges to data science. Recent work in the spirit of computational social science returns to critical approach to operationalisation providing a fresh perspective on this important topic. In this chapter we highlight works that examines gender computationally, describing how they employ levels of feminist theory to challenge gender inequality at the micro, meso, and macro level. We argue that paying critical attention to how we infer and analyze gender is fruitfully in understanding society and the contributions of research. We also present various sources and methods to infer gender and provide examples of the application of such methods. We conclude by outlining the way forward for computational methods in how gender and intersectional inequality is studied.This is a draft. The final version will be available in Handbook of Computational Social Science edited by Taha Yasseri, forthcoming 2023, Edward Elgar Publishing Ltd. The material cannot be used for any other purpose without further permission of the publisher and is for private use only. Please cite as: Vasarhelyi, O., & Brooke, S.(2023). Computing Gender. In: T. Yasseri (Ed.), Handbook of Computational Social Science. Edward Elgar Publishing Ltd.
Authors: Orsolya Vasarhelyi, Siân Brooke
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: gold
OpenAlex ID: W4407080776