  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.
