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Hannah L. Brown, Chase R. Booth, Elizabeth G. Eason and Assistant Professor Damian G. Kelty-Stephen

This gender study exemplifies fields struggling to balance the deeply ingrained desire for logical formalisms and conceptually dynamic models of systems. Gender Studies grounds itself in dynamic models as seen in the popularity of ‘intersectionality theory,’ a notion of experiences as unfolding at the ‘intersections’ of classical taxonomies. This popular theory evades quantitative research because it eschews classical categorical distinctions. The authors introduce multifractal analysis and suggest that cascade dynamics and multifractal analysis provide logical and corresponding statistical frameworks to make intersectionality quantitatively and tractably expressible for gendered experiences. Recent cognitive science advances involve multifractal analysis laying bare key features of the cascades driving cognitive performance. The chapter offers similar demonstration of similar cascades in gender dynamics through multifractal analysis of web-traffic data for gender terms on Wikipedia. It concludes that cascade formalisms and multifractal analysis offer new avenues for gender studies balancing both logical formalisms and dynamic concepts.