TY - UNPB
T1 - Developing an occupational prestige scale using Large Language Models
AU - de Vries, Robert
AU - Hill, Mark
AU - Ruis, Laura
PY - 2024
Y1 - 2024
N2 - Large Language Models (LLMs), being trained on large fractions of all online text, reflect societal biases and stereotypes – such as racial and gender biases. In this paper, we propose a method of using such models to capture societal perceptions of occupational prestige. We create four occupational prestige scales using this method, with each tapping a difference facet of prestige perceptions. These scales are validated against existing prestige scales based on human data. We conclude that it is possible to create valid measures of occupational prestige by prompting commercially available LLMs – though with some important limitations. Implications for future social stratification research are discussed.
AB - Large Language Models (LLMs), being trained on large fractions of all online text, reflect societal biases and stereotypes – such as racial and gender biases. In this paper, we propose a method of using such models to capture societal perceptions of occupational prestige. We create four occupational prestige scales using this method, with each tapping a difference facet of prestige perceptions. These scales are validated against existing prestige scales based on human data. We conclude that it is possible to create valid measures of occupational prestige by prompting commercially available LLMs – though with some important limitations. Implications for future social stratification research are discussed.
U2 - 10.31235/osf.io/ct4vz
DO - 10.31235/osf.io/ct4vz
M3 - Working paper
BT - Developing an occupational prestige scale using Large Language Models
ER -