Computational Analysis of Gender Depiction in the Comedias of Calderón de la Barca
This work addresses the problem of understanding gender norms in historical literature for researchers in digital humanities and literary studies, but it is incremental as it applies existing computational methods to a new dataset.
The study developed quantitative methods to analyze gender depiction in Calderón de la Barca's comedias, using a gender classifier and explainability techniques to identify gendered dialogue elements, achieving up to f=0.83 accuracy in gender prediction and enabling scene-by-scene detection of cross-dressing characters.
In theatre, playwrights use the portrayal of characters to explore culturally based gender norms. In this paper, we develop quantitative methods to study gender depiction in the non-religious works (comedias) of Pedro Calderón de la Barca, a prolific Spanish 17th century author. We gather insights from a corpus of more than 100 plays by using a gender classifier and applying model explainability (attribution) methods to determine which text features are most influential in the model's decision to classify speech as 'male' or 'female', indicating the most gendered elements of dialogue in Calderón's comedias in a human accessible manner. We find that female and male characters are portrayed differently and can be identified by the gender prediction model at practically useful accuracies (up to f=0.83). Analysis reveals semantic aspects of gender portrayal, and demonstrates that the model is even useful in providing a relatively accurate scene-by-scene prediction of cross-dressing characters.