This study investigates the stereotypical portrayal of female major characters (FMCs) in fictional literature, with a focus on the roles of author gender and literary genre. Drawing on a dataset of 13.869 novels published between 1485 and 2025, with the majority originating from 1975 onwards, the analysis examines whether male authors represent FMCs in ways that align with or challenge traditional gender stereotypes regarding warmth-communality, agency-competence, appearance-focus, and occupational rank, after controlling for literary genre. To assess these portrayals, character descriptions were evaluated using a large-language-model-based AI agent, allowing for scalable, consistent judgments of stereotypical content across a broad range of books. Findings suggest that author gender does not have a strong effect on the degree of stereotypical characterization. Contrary to expectations, male authors in the sample tended to depict FMCs as slightly less stereotypical than their female counterparts, though this result should be interpreted with caution due to potential sampling limitations. In contrast, literary genre proved to be a significant factor: genres such as Historical Fiction, Romance, and Literary Fiction tended to include more pro-stereotypical portrayals, while Science Fiction, Fantasy, Thriller, and Mystery more often featured anti-stereotypical representations. The results emphasize the importance of genre conventions in shaping FMC portrayals and underscore the need for awareness among readers, writers, and publishers regarding the ways in which literature can reflect or challenge gender norms.

Keywords: gender stereotypes, computational literary studies, female characters, literary genre, author gender, AI content analysis, large language models