Gaydar and the Fallacy of Decontextualized Measurement

By Andrew Gelman, Greggor Mattson, Daniel P. Simpson. 2018

16 citations recorded by the source corpus.

Recent media coverage of studies about 'gaydar,' the supposed ability to detect another's sexual orientation through visual cues, reveal problems in which the ideals of scientific precision strip the context from intrinsically social phenomena. This fallacy of objective measurement, as we term it, leads to nonsensical claims based on the predictive accuracy of statistical significance. We interrogate these gaydar studies' assumption that there is some sort of pure biological measure of perception of sexual orientation. Instead, we argue that the concept of gaydar inherently exists within a social context and that this should be recognized when studying it. We use this case as an example of a more general concern about illusory precision in the measurement of social phenomena and suggest statistical strategies to address common problems.

Subfields: Anthropology, Archeology, Safety, Robustness & Interpretability

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Citations in the corpus

Diagnosing Gender Bias in Image Recognition Systems (2020, background): “…in commercial enterprise (e.g., Greenfield 2018; HG Insights 2020) and, increasingly, for social research (e.g., Di Ronco and Allen-Robertson 2020; Garimella and Eckles 2020; Geboers and Van de Wiele 2020; Gelman, Mattson, and Simpson 2018; Webb Williams, Casas, and Wilkerson 2020; Xi et al. 2019).”
Data and its (dis)contents: A survey of dataset development and use in machine learning research (2020, background): “discuss how such a study strips away context and implies the existence of an ‘‘essential homosexual nature’’.(43) The task rests on a pseudoscientific essentialism of human traits.”