arXiv:2607.02459v1 Announce Type: new Abstract: Language models are increasingly used to quantify cultural phenomena, but what makes such measurement distinctively cultural? This paper argues that NLP work on culture is a material-discursive practice: the apparatus -- model, data, annotation, evaluation -- participates in constituting the cultural reality it measures, rather than passively recording it. Drawing on Karen Barad's concept of the agential cut -- the contingent boundary between phenomenon and instrument -- I show that the apparatus's substantive design choices draw such boundaries,
The proliferation of Large Language Models (LLMs) in various research domains, including social sciences, is driving a critical examination of their methodological implications and biases.
This paper highlights that LLMs are not neutral tools for cultural measurement but actively shape the 'cultural reality' they quantify, forcing a re-evaluation of current research practices and findings.
Researchers and policymakers will need to adopt a more critical approach to how LLMs are designed, trained, and interpreted when used to analyze or define cultural phenomena, understanding their inherent biases as constitutive.
- · Ethical AI researchers
- · Digital humanities
- · Social scientists
- · Cultural theorists
- · Uncritical adopters of AI for social research
- · Researchers relying on black-box LLMs
- · Entities commissioning un-audited cultural impact reports
Increased scrutiny and methodological refinement in AI-driven social and cultural research.
Development of new interpretative frameworks and auditing tools for opaque AI models in humanistic studies.
Potential for a 'cultural relativism' debate within AI ethics, questioning universal metrics or definitions derived from LLMs.
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Read at arXiv cs.CL