社会的技术构建:比较 GPT-4 和英国职业评估中的人类受访者

IF 1.3 2区 管理学 Q3 INDUSTRIAL RELATIONS & LABOR
Paweł Gmyrek, Christoph Lutz, Gemma Newlands
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引用次数: 0

摘要

尽管对大型语言模型(LLMs)的偏差和认知进行了初步研究,但我们缺乏有关 LLMs 如何评估职业的证据,尤其是与人类评估者相比。在本文中,我们系统地比较了 GPT-4 与英国近期一项深入、高质量的人类受访者调查中的职业评价。我们的研究结果表明,在所有 ISCO-08 主要组别中,GPT-4 和人类评分高度相关。与此同时,GPT-4 大大低估或高估了许多职业的职业声望和社会价值,尤其是新兴数字职业和被污名化或非法的职业。我们的分析表明了将 LLM 生成的数据用于社会学和职业研究的潜力和风险。我们还讨论了我们的研究结果对将 LLM 工具融入工作领域的政策影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A technological construction of society: Comparing GPT‐4 and human respondents for occupational evaluation in the UK
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.
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来源期刊
British Journal of Industrial Relations
British Journal of Industrial Relations INDUSTRIAL RELATIONS & LABOR-
CiteScore
4.20
自引率
11.50%
发文量
58
期刊介绍: BJIR (British Journal of Industrial Relations) is an influential and authoritative journal which is essential reading for all academics and practitioners interested in work and employment relations. It is the highest ranked European journal in the Industrial Relations & Labour category of the Social Sciences Citation Index. BJIR aims to present the latest research on developments on employment and work from across the globe that appeal to an international readership. Contributions are drawn from all of the main social science disciplines, deal with a broad range of employment topics and express a range of viewpoints.
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