 A technological construction of society: Comparing GPT‐4 and human respondents for occupational evaluation in the UKPaweł Gmyrek, Christoph Lutz and Gemma Newlands British Journal of Industrial Relations, 2025, vol. 63, issue 1, 180-208 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. Date: 2025 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:bla:brjirl:v:63:y:2025:i:1:p:180-208 Ordering information: This journal article can be ordered from Access Statistics for this article British Journal of Industrial Relations is currently edited by Edmund Heery More articles in British Journal of Industrial Relations from London School of Economics Contact information at EDIRC. |
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