  Probabilistic methods for classifying text form a rich tradition in machine
learning and natural language processing. For many important problems, however,
class prediction is uninteresting because the class is known, and instead the
focus shifts to estimating latent quantities related to the text, such as
affect or ideology. We focus on one such problem of interest, estimating the
ideological positions of 55 Irish legislators in the 1991 D\'ail confidence
vote. To solve the D\'ail scaling problem and others like it, we develop a text
modeling framework that allows actors to take latent positions on a "gray"
spectrum between "black" and "white" polar opposites. We are able to validate
results from this model by measuring the influences exhibited by individual
words, and we are able to quantify the uncertainty in the scaling estimates by
using a sentence-level block bootstrap. Applying our method to the D\'ail
debate, we are able to scale the legislators between extreme pro-government and
pro-opposition in a way that reveals nuances in their speeches not captured by
their votes or party affiliations.
