Playing on hard: Algorithmic border objects and inequality among esports student-athletes

Abstract

Collegiate esports are a key contributor to the North American esports field’s fledgling talent pipeline, where varsity student-athletes identify the streaming platform Twitch as a major component. Exemplified by Twitch, this article theorizes the role of platform algorithms as border objects—an analytical concept which frames the shared use of classification systems when a powerful party’s practices naturalize their interpretation over others. Twitch’s platform recommendation and moderation algorithms are classifiers used by competitive game-content creators and platform owners. Its algorithms are fundamental to allocating visibility among users, which, as collegiate esports players suggest, informs professional progress. However, algorithms have proven to perpetuate and exacerbate the exclusion of marginalized persons from platforms. Drawing on ethnographic interviews, participant observation, and existing scholarship, this article argues that the inherent biases of platform architecture in esports’ talent pipeline upholds patriarchal structures and reinforces inequality—reducing opportunities for diversity and equality in esports.