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Wordfish Scaling

Wordfish scaling is an unsupervised text-as-data method that estimates a single latent position for each political document — a party manifesto, a legislative speech, a press release — directly from its word frequencies, without any reference texts or hand coding. Introduced by Slapin and Proksch in 2008, it models word counts as draws from a Poisson distribution whose rate depends on a document position and word-specific parameters, recovering, for example, a left–right ordering of parties purely from how often each word appears in each text.

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Sources

  1. Slapin, J. B., & Proksch, S.-O. (2008). A Scaling Model for Estimating Time-Series Party Positions from Texts. American Journal of Political Science, 52(3), 705–722. DOI: 10.1111/j.1540-5907.2008.00338.x
  2. Lowe, W., & Benoit, K. (2013). Validating Estimates of Latent Traits from Textual Data Using Human Judgment as a Benchmark. Political Analysis, 21(3), 298–313. DOI: 10.1093/pan/mpt002
  3. Grimmer, J., & Stewart, B. M. (2013). Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts. Political Analysis, 21(3), 267–297. DOI: 10.1093/pan/mps028

How to cite this page

ScholarGate. (2026, June 22). Wordfish Scaling of Political Texts (Unsupervised Position Estimation). ScholarGate. https://scholargate.app/en/political-science/wordfish-scaling

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ScholarGateWordfish Scaling (Wordfish Scaling of Political Texts (Unsupervised Position Estimation)). Retrieved 2026-06-24 from https://scholargate.app/en/political-science/wordfish-scaling · Dataset: https://doi.org/10.5281/zenodo.20539026