Comparing Architectures for Supervised Political Scaling
Computation and Language
2026-07-01 v1
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?
Cite
@article{arxiv.2607.01464,
title = {Comparing Architectures for Supervised Political Scaling},
author = {Anna Golub and Sebastian Padó},
journal= {arXiv preprint arXiv:2607.01464},
year = {2026}
}