How language shapes prejudice against women: An examination across 45 world languages.

By David DeFranza, Himanshu Mishra, Arul Mishra. 2020

61 citations recorded by the source corpus.

Language provides an ever-present context for our cognitions and has the ability to shape them. Languages across the world can be gendered (language in which the form of noun, verb, or pronoun is presented as female or male) versus genderless. In an ongoing debate, one stream of research suggests that gendered languages are more likely to display gender prejudice than genderless languages. However, another stream of research suggests that language does not have the ability to shape gender prejudice. In this research, we contribute to the debate by using a Natural Language Processing (NLP) method which captures the meaning of a word from the context in which it occurs. Using text data from Wikipedia and the Common Crawl project (that contains text from billions of publicly facing websites) across 45 world languages, covering the majority of the world's population, we test for gender prejudice in gendered and genderless languages. We find that gender prejudice occurs more in gendered rather than genderless languages. Moreover, we examine whether genderedness of language influences the stereotypic dimensions of warmth and competence utilizing the same NLP method. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Subfields: Gender Studies, Natural Language Processing

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Citations in the corpus

Based on billions of words on the internet, people = men (2022, methodology): “We conducted another replication of Study 1 using identical lists of words and other procedures to Study 1, with one exception: We used a different set of word embeddings ( 53 ).”
Gender Stereotypes in Natural Language: Word Embeddings Show Robust Consistency Across Child and Adult Language Corpora of More Than 65 Million Words (2021, unknown): “Notably, we also performed a supplementary analysis to test the robustness of the results when using other (longer) word stimuli lists, obtained from a more recent application of the WEAT (DeFranza et al., 2020).”