AI-assisted exploration reveals author gender has minor effects on character portrayals in various genres.
This study investigates the stereotypical portrayal of female major characters (FMCs) in fiction books. Building on prior indications that male authors create relatively stereotypical FMCs, this study analyzes public data and reader perceptions of 13,869 novels published between 1485 and 2025, with the majority originating from 1975 onwards, to examine whether the effect of author gender might be explained by genre confounding. FMC stereotypicality was measured on four dimensions: warmth-communion, agency-competence, occupational rank, and appearance-focus with all character annotations being collected by a search-enabled AI agent. Human-in-the-loop-validation of the agent’s search traces indicated an annotation accuracy between 80% and 100%, depending on the annotation task. Books in Historical Fiction, Romance, and Literary Fiction featured relatively stereotypical FMCs, while Science Fiction, Fantasy, Thriller, and Mystery books included more counter-stereotypical FMCs. Further, stereotyping clearly decreased over time. Contrary to previous work, effects of author gender were small and multi-directional. In most genres, author gender did not predict stereotypical portrayals. On average, secondary sources about works by male authors described slightly more agentic/competent FMCs but also revealed more emphasis on characters’ physical beauty. Literary genre and publication year were much better predictors of character stereotypicality than author gender. The use of AI annotators proved useful, but the reliance on secondary sources requires thoughtfully formulated annotation items and limits the study to widely read books with sufficient online material.
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Kratzke et al. (2026) studied this question.
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