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Area of Science:

  • Psychology
  • Linguistics
  • Computational Social Science

Background:

  • Speciesism, a form of prejudice, is often linked to biased language use.
  • Previous research has focused on individual thoughts rather than collective language systems.
  • Understanding speciesism in language is crucial for addressing prejudice.

Purpose of the Study:

  • To quantitatively assess speciesism in language on a large scale.
  • To investigate collective biases in word associations across various media.
  • To determine if language reflects anthropocentric and companion animal speciesism.

Main Methods:

  • Utilized machine learning techniques, specifically word embeddings.
  • Analyzed billions of English words from diverse sources (conversation, film, books, internet).
  • Quantified associations between animal terms and words indicating concern, value, or indifference.

Main Results:

  • Found evidence of anthropocentric speciesism: humans were more closely associated with positive terms (concern, value) than most other animals.
  • Identified companion animal speciesism: companion animals showed closer associations with positive terms than most other animals.
  • Demonstrated pervasive speciesism embedded within everyday language.

Conclusions:

  • Speciesism is a collective phenomenon evident in natural language use.
  • Language reflects and potentially perpetuates biased attitudes towards different animal groups.
  • This research provides a novel, large-scale linguistic perspective on speciesism.