A new computational analysis of over two centuries of UK parliamentary debates reveals persistent sexism in language used to discuss women's representation. Researchers utilized large language models to classify speeches from the Hansard Corpus (1803-2005), finding that 54% of speeches opposing women's representation contained sexist content, compared to 21% of those in favor. The study also identified distinct types of sexism used by each side, with pro-suffrage sexism being predominantly benevolent, while anti-suffrage rhetoric combined hostile and benevolent framing. The analysis further showed that female MPs were more likely to support women's political rights than male MPs, a gap that narrowed after women gained suffrage. AI
IMPACT Highlights how LLMs can be used to analyze historical linguistic patterns and uncover societal biases.
RANK_REASON Academic paper detailing computational analysis of historical text data. [lever_c_demoted from research: ic=1 ai=1.0]
- alphaXiv
- Ambivalent Sexism Inventory
- CatalyzeX
- DagsHub
- Gotit.pub
- Hansard Corpus
- House of Commons
- Hugging Face
- ScienceCast
- UK
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