How Stereotypes Are Shared Through Language: A Review and Introduction of the Social Categories and Stereotypes Communication (SCSC) Framework

By Camiel J. Beukeboom, Christian Burgers. 2019

135 citations recorded by the source corpus.

Language use plays a crucial role in the consensualization of stereotypes within cultural groups. Based on an integrative review of the literature on stereotyping and biased language use, we propose the Social Categories and Stereotypes Communication (SCSC) framework. The framework integrates largely independent areas of literature and explicates the linguistic processes through which social-category stereotypes are shared and maintained. We distinguish two groups of biases in language use that jointly feed and maintain three fundamental cognitive variables in (shared) social-category cognition: perceived category entitativity, stereotype content, and perceived essentialism of associated stereotypic characteristics. These are: (1) Biases in linguistic labels used to denote categories, within which we discuss biases in (a) label content and (b) linguistic form of labels; (2) Biases in describing behaviors and characteristics of categorized individuals, within which we discuss biases in (a) communication content (i.e., what information is communicated), and (b) linguistic form of descriptions (i.e., how is information formulated). Together, these biases create a self-perpetuating cycle in which social-category stereotypes are shared and maintained. The framework allows for a better understanding of stereotype maintaining biases in natural language. We discuss various opportunities for further research.

Subfields: Experimental and Cognitive Psychology, Language and Linguistics, Sociology and Political Science, Natural Language Processing, Safety, Robustness & Interpretability

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Citations in the corpus

Language (Technology) is Power: A Critical Survey of “Bias” in NLP (2020, background): “Group labels can serve as the basis of stereotypes and thus reinforce social inequalities: “[T]he label content functions to identify a given category of people, and thereby conveys category boundaries and a position in a hierarchical taxonomy” (Beukeboom and Burgers, 2019).”
Dialect Diversity in Text Summarization on Twitter (2020, background): “[5] Camiel J Beukeboom and Christian Burgers.”
Towards Ethics by Design in Online Abusive Content Detection (2020, background): “Delineating target categories based on identity groups, selecting search terms associated with the groups, and annotating for ﬁne-grained target categories can propagate harmful stereotypes and re-inforce social iniquity (Beukeboom and Burgers, 2019).”
Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond (2023, background): “LLMs have been shown to exhibit disparate treatment and impact, perpetuating societal biases and potentially leading to discrimination [10, 17].”
Gender Bias in Machine Translation (2021, background): “In a community increasingly conscious of the power of language to hardwire stereotypical beliefs and women’s invisibility (Lindqvist et al., 2019; Beukeboom and Burgers, 2019), the bias exhibited by the system was confronted with a new sensitivity.”
Stepmothers are mean and academics are pretentious: What do pretrained language models learn about you? (2021, unknown): “Psychology studies show that beliefs about social groups are transmitted and shaped through language (Maass, 1999; Beukeboom and Burgers, 2019).”