手法を比較

	Manifesto Coding	Wordfish Scaling
分野	Political Science	Political Science
系統	Process / pipeline	Latent structure
提唱年	2001	2008
提唱者	Manifesto Research Group / Comparative Manifesto Project (CMP/MARPOR)	Jonathan Slapin and Sven-Oliver Proksch
種類	Quantitative content analysis of party manifestos	Unsupervised latent-position model for word-count data
原典	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	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.
別名	CMP coding, MARPOR coding, Manifesto content analysis, Party manifesto coding	Wordfish text scaling, Poisson scaling of texts, Unsupervised text scaling, Wordfish position estimation
関連	4	4
概要	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.	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.