方法对比

	Wordfish Scaling	Manifesto Coding
领域	Political Science	Political Science
方法族	Latent structure	Process / pipeline
起源年份	2008	2001
提出者	Jonathan Slapin and Sven-Oliver Proksch	Manifesto Research Group / Comparative Manifesto Project (CMP/MARPOR)
类型	Unsupervised latent-position model for word-count data	Quantitative content analysis of party manifestos
开创性文献	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.	Budge, I., Klingemann, H.-D., Volkens, A., Bara, J., & Tanenbaum, E. (2001). Mapping Policy Preferences: Estimates for Parties, Electors, and Governments 1945–1998. Oxford: Oxford University Press. ISBN: 9780199244003
别名	Wordfish text scaling, Poisson scaling of texts, Unsupervised text scaling, Wordfish position estimation	CMP coding, MARPOR coding, Manifesto content analysis, Party manifesto coding
相关	4	4
摘要	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.	Manifesto coding is the quantitative content-analysis methodology of the Comparative Manifesto Project (CMP/MARPOR) for measuring parties' policy preferences from their election manifestos. Trained coders break each manifesto into quasi-sentences and assign every unit to one of a fixed set of policy categories. Counting how often each category appears yields salience measures, and combining pro- and anti- categories produces position scores such as the left–right RILE index, giving comparable estimates of party positions across more than fifty democracies since 1945.