Quantitative researchers lack comprehensive 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 is consistent with a constructivist framework that views gender as a set of multidimensional attributes that differentiate men and women in particular societies. Using this framework and the 2018 Program for International Student Assessment (PISA 2018) dataset, I identified the constitutive elements of gender across 72 countries and 75 constructs using a structure learning algorithm. I also examined whether country-level characteristics predict the presence of competitiveness –considered a key gender-differentiating attribute– as a constitutive element of gender. The results indicate that in nations where women have greater access to educational and economic opportunities, competitiveness becomes a stronger gender-differentiating factor. I discuss the advantages, assumptions, and limitations of the proposed approach to operationalize gender.