Comparing Architectures for Supervised Political Scaling
Anna Golub, Sebastian Padó
cs.CL
Abstract
Text scaling, the task of positioning political actors on an ideological scale, is a fundamental task in political analysis. To ease the need for manual analysis, various NLP methods have been proposed for this task, including classification- and regression-based approaches, showing successes as well as limitations. The goal of our paper is to consolidate the state of the art in this area. We ask two questions: (a) Can the performance of scaling methods be improved by predicting scales not individually but jointly? (b) Is there a middle ground between classification and regression?
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Classified with taxonomy v2 on Sat, 5 Sept 2026.