Using data from the 2018 National Financial Capability Study (NFCS), this study examined the relationships between poor financial behaviors, receiving government assistance, and financial satisfaction while accounting for adverse financial experiences. The logistic regression results showed that both poor financial behaviors and adverse financial experiences increased the likelihood of receiving government assistance. The OLS results indicated that receiving government assistance significantly increased levels of financial satisfaction, whereas poor financial behaviors significantly decreased levels of financial satisfaction. While the magnitude of these associations for both receiving government assistance and poor financial behaviors was small, adverse financial experiences had a stronger influence on the levels of financial satisfaction. When we combined poor financial behaviors and receiving government assistance into a categorical variable, we gained additional insights into the connections between these constructs that warrants further research.

Cross-referencing, which links passages of text to other related passages, can be a valuable study aid for facilitating comprehension of a text. However, cross-referencing requires first, a comprehensive thematic knowledge of the entire corpus, and second, a focused search through the corpus specifically to find such useful connections. Due to this, crossreference resources are prohibitively expensive and exist only for the most well-studied texts (e.g. religious texts). We develop a topic-based system for automatically producing candidate cross-references which can be easily verified by human annotators. Our system utilizes fine-grained topic modeling with thousands of highly nuanced and specific topics to identify verse pairs which are topically related. We demonstrate that our system can be cost effective compared to having annotators acquire the expertise necessary to produce cross-reference resources unaided.

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We propose Labeled Anchors, an interactive and supervised topic model based on the anchor words algorithm (Arora et al., 2013). Labeled Anchors is similar to Supervised Anchors (Nguyen et al., 2014) in that it extends the vector-space representation of words to include document labels. However, our formulation also admits a classifier which requires no training beyond inferring topics, which means our approach is also fast enough to be interactive. We run a small user study that demonstrates that untrained users can interactively update topics in order to improve classification accuracy.