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Comparing Architectures for Supervised Political Scaling

arXiv 2607.01464 ↗First posted 1 Jul 2026cs.CLcited by 0

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Abstract (arXiv)

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?

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 49 references that are in the map. First posted on arXiv 1 Jul 2026.

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References · 14

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
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  8. 2024Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inferencecited by 527
  9. 2024mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrievalcited by 233
  10. 2021Whitening Sentence Representations for Better Semantics and Faster Retrievalcited by 198
  11. 2025On the Emergence of Position Bias in Transformerscited by 57
  12. 2019Political Text Scaling Meets Computational Semanticscited by 10
2 more
  1. 2022Optimizing text representations to capture (dis)similarity between political partiescited by 6
  2. 2023Additive manifesto decomposition: A policy domain aware method for understanding party positioningcited by 5

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