So sánh phương pháp

	Wordfish Scaling	Manifesto Coding	Wordfish
Lĩnh vực	Political Science	Political Science	Trắc lượng tâm lý
Họ	Latent structure	Process / pipeline	Latent structure
Năm ra đời	2008	2001	2008
Người khởi xướng	Jonathan Slapin and Sven-Oliver Proksch	Manifesto Research Group / Comparative Manifesto Project (CMP/MARPOR)	Jonathan Slapin, Svenja-Sophia Proksch
Loại	Unsupervised latent-position model for word-count data	Quantitative content analysis of party manifestos	Generative text model for dimension reduction
Công trình gốc	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	Slapin, J. B., & Proksch, S. O. (2008). A scaling model for estimating time-series party positions from texts. Journal of Politics, 70(3), 554-569.
Tên gọi khác	Wordfish text scaling, Poisson scaling of texts, Unsupervised text scaling, Wordfish position estimation	CMP coding, MARPOR coding, Manifesto content analysis, Party manifesto coding	—
Liên quan	4	4	5
Tóm tắt	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.	Wordfish is a statistical model for scaling documents on latent dimensions, developed by Slapin and Proksch (2008). Unlike reference-based methods like Wordscores, Wordfish uses a Poisson generative model to jointly estimate word frequencies and document positions without requiring reference texts or manual annotation. It is particularly useful for estimating time-series changes in policy positions and can scale documents from multiple languages simultaneously.