Gender inequity is one of the biggest challenges facing the STEM workforce. While there are many studies that look into gender disparities within STEM and academia, the majority of these have been designed and executed by those unfamiliar with research in sociology and gender studies. They adopt a normative view of gender as a binary choice of 'male' or 'female,' leaving individuals whose genders do not fit within that model out of such research entirely. This especially impacts those experiencing multiple axes of marginalization, such as race, disability, and socioeconomic status. For STEM fields to recruit and retain members of historically excluded groups, a new paradigm must be developed. Here, we collate a new dataset of the methods used in 119 past studies of gender equity, and recommend better survey practices and institutional policies based on a more complex and accurate approach to gender. We find that problematic approaches to gender in surveys can be classified into 5 main themes - treating gender as white, observable, discrete, as a statistic, and as inconsequential. We recommend allowing self-reporting of gender and never automating gender assignment within research. This work identifies the key areas of development for studies of gender-based inclusion within STEM, and provides recommended solutions to support the methodological uplift required for this work to be both scientifically sound and fully inclusive.

翻译：性别不平等是STEM劳动力面临的最大挑战之一。尽管有许多研究关注STEM及学术界中的性别差异，但其中绝大部分是由不熟悉社会学和性别研究的人员设计和执行的。这些研究采用二元化的规范性别观，将性别简化为"男性"或"女性"的选择，从而完全排除了性别不符合该模型的个体。这尤其影响到那些遭受种族、残疾和社会经济地位等多重边缘化因素的群体。为使STEM领域能够招募和留住历史上被排斥群体的成员，必须建立新的研究范式。本文整理了一个包含119项过往性别平等研究方法的全新数据集，并基于更复杂和准确的性别研究方法，推荐了更优的调查实践和制度政策。我们发现，调查中对性别的处理方式可归纳为五个主要问题类型：将性别视为白人属性、可观察属性、离散属性、统计变量以及无关变量。我们建议在研究过程中允许性别自报，绝不自动分配性别。本研究明确了STEM领域基于性别的包容性研究的关键发展方向，并提供了推荐解决方案，以支持该方法论提升工作，确保相关研究既具有科学严谨性又完全包容。