The Model as One Rater Among Several: Measuring Political Positions in Data-Sparse Regions with a Language-Model Panel

The paper introduces a novel method for measuring political positions in data-sparse regions, utilizing a large language model as one of several raters in a panel approach, rather than as a standalone measurement tool. Key findings include a mean score adjustment of 1.8 points on a 21-point scale when adding written axis definitions, high reliability with Krippendorff's alpha of 0.86 across multiple models, and insights into interpretative disagreements within the panel. This approach is significant for AI practitioners as it provides a framework for leveraging language models in diverse political contexts where traditional tools may fail, while also emphasizing the importance of human validation in the process.