Recent years have seen the rise of subject-themed digital libraries, such as the NSDL pathways and the Digital Library for Earth System Education (DLESE). These libraries often need to manually verify that contributed resources cover top- ics that fit within the theme of the library. We show that such scope judgments can be automated using a combination of text classification techniques and topic modeling. Our models address two significant challenges in making scope judgments: only a small number of out-of-scope resources are typically available, and the topic distinctions required for digital libraries are much more subtle than classic text classification problems. To meet these challenges, our mod- els combine support vector machine learners optimized to diffierent performance metrics and semantic topics induced by unsupervised statistical topic models. Our best model\ is able to distinguish resources that belong in DLESE from resources that don't with an accuracy of around 70%. We see these models as the first steps towards increasing the scalability of digital libraries and dramatically reducing the workload required to maintain them.