Experiences with Misgendering: Identity Misclassification of Transgender Spectrum Individuals

By Kevin A. McLemore. 2015

191 citations recorded by the source corpus.

Subfields: Social Psychology, Sociology and Political Science, Natural Language Processing, Safety, Robustness & Interpretability

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Citations in the corpus

Toward Gender-Inclusive Coreference Resolution (2019, background): “fect alignment with linguistic gender, it is generally clear that an incorrectly gendered reference to a person (whether through pronominalization or otherwise) can be highly problematic (Johnson et al., 2019; McLemore, 2015).”
Revisiting Gendered Web Forms: An Evaluation of Gender Inputs with (Non-)Binary People (2021, unknown): “Motivated by previous work on non-binary representation in technical infrastructures, as well as research discussing the damage misgendering2 and living as an inauthentic gender can have on trans individuals [45, 64], we sought out opinions of binary (both cisgender and trans) and non-binary individ”
Health Care Experiences of Transgender Binary and Nonbinary University Students (2019, background): “In one study, nonbinary people reported more frequent misgendering (not being “read” as one’s affirmed gender) than trans men or trans women (McLemore, 2014).”
How Computers See Gender (2019, background): “It is not hard to imagine how the proliferation of large scale services like the ones we have studied could also scale experiences of misgendering documented by others [53, 74].”
HCI Guidelines for Gender Equity and Inclusivity (2020, background): “Research has shown that misgendering decreases self-worth, increases felt stigma and reduces overall wellbeing [25,33].”
The Misgendering Machines (2018, background): “The experience of being misgendered by people has been shown to prime individuals for rejection [85], impact self-esteem and felt authenticity, and increase one’s perception of being socially stigmatised [60].”
Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluation (2022, background): “While on some dataset and algorithm combinations it is best to give the algorithm the most granular set of racial labels, in others it can actually benefit to use a more coarse set of labels. of which have associated harms [4, 13, 54, 93, 107, 108].”
A Minority Stress Perspective on Transgender Individuals’ Experiences With Misgendering (2016, methodology): “Replicating previous research (McLemore, 2015), participants were expected to frequently report being misgendered and feeling stigmatized by these experiences (Hypothesis 1).”
Transgender youth homelessness: Understanding programmatic barriers through the lens of cisgenderism (2015, background): “The questioning can be understood as the pathologizing form of cisgenderism (McLemore, 2014; Riggs et al., 2015; Ansara & Hegarty, 2014).”