A technological construction of society: Comparing GPT‐4 and human respondents for occupational evaluation in the UK
系统比较GPT-4与英国最新高质量调查对580种职业的声望和社会价值评价，发现两者高度相关，但GPT-4对新兴数字职业和污名化职业的评价存在显著偏差，揭示了用大语言模型数据做社会学研究的潜力和风险。
Abstract Despite initial research about the biases and perceptions of large language models (LLMs), we lack evidence on how LLMs evaluate occupations, especially in comparison to human evaluators. In this paper, we present a systematic comparison of occupational evaluations by GPT‐4 with those from an in‐depth, high‐quality and recent human respondents survey in the UK. Covering the full ISCO‐08 occupational landscape, with 580 occupations and two distinct metrics (prestige and social value), our findings indicate that GPT‐4 and human scores are highly correlated across all ISCO‐08 major groups. At the same time, GPT‐4 substantially under‐ or overestimates the occupational prestige and social value of many occupations, particularly for emerging digital and stigmatized or illicit occupations. Our analyses show both the potential and risk of using LLM‐generated data for sociological and occupational research. We also discuss the policy implications of our findings for the integration of LLM tools into the world of work.