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arXiv Machine Learning By Patrick Parschan

Making Political Text Scaling Comparable: Infrastructure and Hyperparameter Sensitivity for 17 Algorithms

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The paper argues that computational text‑based ideal point estimation (CT‑IPE) methods should be viewed as configurable measurement pipelines rather than fixed estimators. It presents a large‑scale comparative experiment involving 17 CT‑IPE algorithms, 5,537 runs, and about 4.25 million left‑right position estimates, and describes shared infrastructure that enables joint execution of these heterogeneous methods. Sensitivity analyses reveal that most algorithms exhibit low hyperparameter sensitivity (ICC < .10), with any remaining sensitivity concentrated in a few key researcher choices such as the language or embedding model, seed keyword lists, and number of topics.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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