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.

中文翻译：

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努力比赛：算法边界对象和电子竞技学生运动员之间的不平等

大学电子竞技是北美电子竞技领域新兴人才输送渠道的关键贡献者，大学学生运动员将流媒体平台 Twitch 视为主要组成部分。本文以 Twitch 为例，将平台算法作为边界对象的作用理论化，这是一种分析概念，当强大一方的做法将其解释自然化于其他方时，该分析概念框架了分类系统的共享使用。 Twitch 的平台推荐和审核算法是竞争性游戏内容创建者和平台所有者使用的分类器。它的算法是在用户之间分配可见性的基础，正如大学电子竞技运动员所说，这可以促进职业进步。然而，事实证明，算法会永久存在并加剧边缘化人群被平台排斥的情况。本文借鉴民族志访谈、参与者观察和现有学术成果，认为电子竞技人才输送平台架构的固有偏见维护了父权结构并加剧了不平等，减少了电子竞技多样性和平等的机会。