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Article Type

Full-Length Paper

Publication Date

2021-11-10

DOI

10.7191/jeslib.2021.1219

Abstract

Objective: Existing studies estimate that between 0.3% and 2% of adults in the U.S. (between 900,000 and 2.6 million in 2020) identify as a nonbinary gender or otherwise gender nonconforming. In response to the RDAP 2021 theme of radical change, this article examines the need to change how datasets represent nonbinary persons and how research involving gender data should approach the curation of this data at each stage of the research lifecycle.

Methods: In this article, we examine some of the known challenges of gender inclusion in datasets and summarize some solutions underway. Using a critical lens, we examine the difference between current practice and inclusive practice in gender representation, describing inclusive practices at each stage of the research lifecycle from writing a data management plan to sharing data.

Results: Data structures that limit gender to “male” and “female” or ontological structures that use mapping to collapse gender demographics to binary values exclude nonbinary and gender diverse populations. Some data collection instruments attempt inclusivity by adding the gender category of “other,” but using the “other” gender category labels nonbinary persons as intrinsically alien. Inclusive change must go farther, to move from alienation to inclusive categories. We describe several techniques for inclusively representing gender in data, from the data management planning stage, to collecting data, cleaning data, and sharing data. To facilitate better sharing of gender data, repositories must also allow mapping that includes nonbinary genders explicitly and allow for ontological mapping for long-term representation of diverse gender identities.

Conclusions: A good practice during research design is to consider two levels of critique in the data collection plan. First, consider the research question at hand and remove unnecessary gendering from the data. Secondly, if the research question needs gender, make sure to include nonbinary genders explicitly. Allies must take on this problem without leaving it to those who are most affected by it. Further, more voices calling for inclusionary practices surrounding data rises to a crescendo that cannot be ignored.

Keywords

gender demographics, nonbinary, transgender, data collection

Acknowledgments

Disclosures: The content of this article is based upon a lightning talk presentation at RDAP Summit 2021 titled “Do I have to be an “other” to be myself?” available at https://osf.io/4duya.

Corresponding Author

Sam A. Leif (they/them), University of Nevada, Las Vegas, Box 451030, 4505 S. Maryland Parkway, Las Vegas, NV 89154 USA; sam.leif@unlv.edu

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Copyright © 2021 Gofman et al. This is an open access article licensed under the terms of the Creative Commons Attribution License.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

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