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This paper consolidates the state of the art in supervised political scaling, investigating whether joint prediction of ideological scales and a middle ground between classification and regression can improve performance.
This paper investigates whether LLMs can identify their own model family from stylometric fingerprints in role-constrained political analysis texts, even after prompt-level anonymization. The findings confirm that anonymization is insufficient and have implications for EU AI Act compliance and multi-agent system validation.