Studies of gendered phenomena online have highlighted important disparities, such as who is likely to be elevated as an expert or face gender-based harassment. This research, however, typically relies upon inferring user gender—an act that perpetuates notions of gender as an easily observable, binary construct. Motivated by work in gender and queer studies, we therefore compare common approaches to gender inference in the context of online settings. We demonstrate that gender inference can have downstream consequences when studying gender inequities and find that nonbinary users are consistently likely to be misgendered or overlooked in analysis. In bringing a theoretical focus to this common methodological task, our contribution is in problematizing common measures of gender, encouraging researchers to think critically about what these constructs can and cannot capture, and calling for more research explicitly focused on gendered experiences beyond a binary.

中文翻译：

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对非分类进行分类：在线研究性别现象的挑战

对在线性别现象的研究凸显了重要的差异，例如谁可能被提升为专家或面临基于性别的骚扰。然而，这项研究通常依赖于推断用户性别——这种行为将性别概念永久化为一种易于观察的二元结构。因此，在性别和酷儿研究工作的推动下，我们比较了在线环境中性别推断的常见方法。我们证明，在研究性别不平等时，性别推断可能会产生下游后果，并发现非二元用户在分析中始终可能被错误性别化或被忽视。在将理论重点放在这一共同的方法论任务上时，我们的贡献在于对常见的性别衡量标准提出问题，鼓励研究人员批判性地思考这些结构可以和不能捕获什么，并呼吁更多的研究明确关注二元之外的性别体验。