2025/09/15 by Sara E. Burke, Alexandria Jaurique, Benjamin M. Valen +3 · 1 voice · 3 citations
Psychology · Social Sciences · #LGBTQ Health, Identity, and Policy #Social and Intergroup Psychology #Names, Identity, and Discrimination Research
paper · doi:10.1177/10888683251360469
Academic AbstractThe present review examines bias against transgender women and men and bias against people with nonbinary gender identities. A central contention is that many people hold false beliefs about transgender men's and women's membership in the categories "male" and "female," and, separately, view nonbinary gender identities themselves as illegitimate. Both discounting transgender men's and women's membership in the categories "male" and "female" and discounting the validity of nonbinary gender identities are forms of bias, and studying them will be essential to build a more nuanced understanding of stereotyping and prejudice. The authors review the literature on bias against transgender people and bias against nonbinary people, discuss the methodological and theoretical challenges in studying these biases, propose an approach for interpreting results in light of common (mis)categorization processes, and consider avenues for future research.Public AbstractThis article reviews research about bias against transgender women and men and bias against people with nonbinary gender identities. Many people hold false beliefs about transgender men's and women's membership in the categories "male" and "female." Also, many people view nonbinary gender identities as illegitimate. Both discounting transgender men's and women's membership in the categories "male" and "female" and discounting the validity of nonbinary gender identities are forms of bias. Thoroughly investigating these forms of bias will help researchers build a more a nuanced understanding of the biases that target transgender and nonbinary people. This observation helps put much of the past research in context, and it could make future research stronger by distinguishing between kinds of bias and encouraging more precise measurement and experimentation.