原型与小说中的性别：基于数据的性别刻板印象在故事中的映射
Archetypes and gender in fiction: A data-driven mapping of gender stereotypes in stories
AI总结：
本文通过数据驱动的方法分析2000个经典男性和女性角色的原型分布，揭示性别刻板印象在故事中的表现，指出性别差异仍维持传统刻板印象。
摘要：
虚构人物的代表反映了社会规范和偏见。例如，无论流派如何，女性在电视和电影中相对代表性不足，并且在这些媒体中经常被刻板印象化。本文基于数据驱动的原型操作化——原型度量学——探讨了2000个经典男性和女性角色的特征。从六个基础原型对的整体空间中，我们发现经典女性角色更倾向于英雄、冒险者、歌星和优雅者原型，而男性角色则倾向于傻瓜、传统主义者、弃儿、暴徒和弃儿类型。然而，性别总体模式仍然维持传统刻板印象：看似积极的英雄偏向女性被女性角色比其他女性角色更男性化所抵消。我们讨论了因角色性别导致的原型代表失衡的社会影响。
英文摘要：
Fictional character representations reflect social norms and biases. For example, women are relatively underrepresented in television and film, irrespective of genre, and are frequently stereotyped in these media. Here, we draw on a data-driven operationalization of archetypes -- archetypometrics -- to explore the characterization of 2,000 canonically male and female characters. From an overall space of six pairs of base archetypes, we find that canonically female characters tend more toward Hero, Adventurer, Diva, and Sophisticate archetypes, while male characters, tend toward Fool, Traditionalist, Outcast, Brute and Outcast types. However, overarching patterns by gender nevertheless sustain traditional stereotypes: The seemingly positive heroic bias toward females is undercut by heroic female characters being more masculine than other female characters. We discuss the societal implications of skewed archetype representation by character gender.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
