{"ID":5675992,"CreatedAt":"2026-07-03T01:40:09.565152011Z","UpdatedAt":"2026-07-04T20:35:29.42772785Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.01464","arxiv_id":"2607.01464","title":"Comparing Architectures for Supervised Political Scaling","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?","short_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. Th...","url_abs":"https://arxiv.org/abs/2607.01464","url_pdf":"https://arxiv.org/pdf/2607.01464v1","authors":"[\"Anna Golub\",\"Sebastian Padó\"]","published":"2026-07-01T20:49:51Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[]","has_code":false}