Quantitative researchers lack frameworks for conceptualizing and operationalizing gender as different from sex. The present study uses the concept of a probabilistic boundary, derived from graphical models, to define the constitutive elements of gender. This approach aligns with a constructivist framework that views gender as a set of multidimensional attributes distinguishing men and women in specific societies. Using this framework and data from the 2018 Program for International Student Assessment (PISA, 2018), I identified the constitutive elements of gender across 72 countries and 74 constructs using a structure learning algorithm. Additionally, I examined whether country-level characteristics predict the presence of competitiveness and empathy—two stereotypically gendered traits—as constitutive elements of gender. The findings suggest that access to educational and economic opportunities and the country's region are predictive of the presence of competitiveness and empathy, respectively. I discuss the advantages, assumptions, and limitations of the proposed approach to operationalize gender.
Rafael Quintana (Tue,) studied this question.