IDEAS home Printed from https://ideas.repec.org/a/bla/brjirl/v63y2025i1p180-208.html
   My bibliography  Save this article

A technological construction of society: Comparing GPT‐4 and human respondents for occupational evaluation in the UK

Author

Listed:
  • Paweł Gmyrek
  • Christoph Lutz
  • Gemma Newlands

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.

Suggested Citation

  • Paweł Gmyrek & Christoph Lutz & Gemma Newlands, 2025. "A technological construction of society: Comparing GPT‐4 and human respondents for occupational evaluation in the UK," British Journal of Industrial Relations, London School of Economics, vol. 63(1), pages 180-208, March.
  • Handle: RePEc:bla:brjirl:v:63:y:2025:i:1:p:180-208
    DOI: 10.1111/bjir.12840
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/bjir.12840
    Download Restriction: no

    File URL: https://libkey.io/10.1111/bjir.12840?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:brjirl:v:63:y:2025:i:1:p:180-208. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/lsepsuk.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.