Mean Girls? The Influence of Gender Portrayals in Teen Movies on Emerging Adults' Gender-Based Attitudes and Beliefs

By Elizabeth Behm-Morawitz, Dana E. Mastro. 2008

180 citations recorded by the source corpus.

Subfields: Gender Studies, Social Sciences & Humanities

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Citations in the corpus

Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies (2021, neutral): “Current language models can perpetrate harms such as the cyclical erasure of non-binary gender identities (Uppunda et al., 2021; Sap, 2021; Lakoff; Fiske, 1993; Fast et al., 2016; Behm-Morawitz and Mastro, 2008).”
ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection (2022, background): “…marginalized groups is often psychologically damaging to members of those groups (Sue et al., 2007; Nadal et al., 2014; Kanter et al., 2017; Nadal, 2018; Saleem and Anderson, 2013) and can reinforce stereotypical or hateful perceptions of them (Behm-Morawitz and Mastro, 2008; Soral et al., 2018).”
Connotation Frames of Power and Agency in Modern Films (2017, background): “A viewer’s impression of a movie character is influenced by how they are written and portrayed, which can in turn influence how people form stereotypes on gender norms (Behm-Morawitz and Mastro, 2008).”
PowerTransformer: Unsupervised Controllable Revision for Biased Language Correction (2020, background): “…2017; Clark et al., 2018, Textio 2 ), models trained on this task could help authors write news, stories, or movies that portray characters in less biased ways, and thereby help mitigate the negative effects of stereotypical portrayals in media (Behm-Morawitz and Mastro, 2008; Field et al., 2019).”
A Survey on Gender Bias in Natural Language Processing (2021, background): “Otherwise, modelling gender as a binary variable can lead to a number of harms such as misgendering and erasure via invalidation or obscuring of non-binary gender identities [Behm-Morawitz and Mastro 2008; Fast et al. 2016].”
Event2Mind: Commonsense Inference on Events, Intents, and Reactions (2018, methodology): “Our Event2Mind inferences automate portrayal analyses that previously required manual annotations (Behm-Morawitz and Mastro, 2008; Prentice and Carranza, 2002; England et al., 2011).”
Large Language Model Alignment: A Survey (2023, background): “; Nadal, 2018; Saleem and Anderson) and may reinforce or amplify existing stereotypes or hateful perceptions about them (Behm-Morawitz and Mastro, 2008; Soral et al., 2018).”
A quantitative analysis of gender differences in movies using psycholinguistic normatives (2015, background): “…topic in the social sciences, with studies conducted on the effect of gender on various aspects of human perception and expression (Benshoff and Grifﬁn, 2011) as well as investigations of the societal (Behm-Morawitz and Mastro, 2008) and career implications of gender and possible underlying biases.”
Shaken and Stirred: A Content Analysis of Women’s Portrayals in James Bond Films (2010, methodology): “For example, utilizing a social cognitive theoretic perspective, Behm-Morawitz and Mastro (2008) found exposure to teen movies could lead to negative stereotypes and attitudes of gender roles and female friendships among high school students.”