Pawel Gmyrek

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.

This paper analyses the relationship between compliance with trade union rights and donors’ aid decisions as evidenced through multi-bilateral contributions to the ILO and a database of Labour Rights’ Indicators. Despite trade union rights being a fundamental worker right that all ILO Member states must uphold, the study finds that the level of labour rights violations is not systematically related to the volume of multi-bi aid channelled through the ILO to recipient countries. Nevertheless, a substantial share of ILO’s voluntary funded programmes implemented in countries with highest labour rights violations targets the strengthening of workers’ organizations, which leads to a discussion on optimal levels of funding in such situations and on conditioning of aid allocation on normative multilateral mechanisms, such as the ILO’s supervisory body.