The contemporary relevance of Adam Smith is evidenced by continued reference to his name. Computational analysis identifies over 700 mentions of Smith and his two famous works—The Theory of Moral Sentiments and The Wealth of Nations—in post-World War 1 House of Commons debates. We find some parliamentarians appreciate Smith’s complex ideas, but most references are ‘ornamental’. Charting Smith’s use over the decades, this paper builds on Kirk Willis’ idea that studying parliamentary debates are an ideal way to understand how, at best, policy ideas, germinate and disseminate over time, or, at worst, how ‘complex ideas became slogans’.

Discrimination against minority ethnic groups has often been analyzed through the lenses of institutional action, antiminority rhetoric, and media framing in the countries of residence. However, we know little about how ethnic discrimination is approached in the home countries of these minorities. To address this gap in the literature, our article seeks to explain how Romanian parliamentarians address discrimination against coethnics abroad. We use inductive thematic analysis conducted on all of the parliamentary speeches about the discrimination of coethnics abroad in the Chamber of Deputies between 2008 and 2020. Our results indicate a relatively high degree of descriptive representation and an extensive concern about discrimination of coethnics abroad from parliamentarians across the political spectrum. The speeches about discrimination differ in content relative to the coethnic community of reference.

We introduce and assess the use of supervised learning in cross-domain topic classification. In this approach, an algorithm learns to classify topics in a labeled source corpus and then extrapolates topics in an unlabeled target corpus from another domain. The ability to use existing training data makes this method significantly more efficient than within-domain supervised learning. It also has three advantages over unsupervised topic models: the method can be more specifically targeted to a research question and the resulting topics are easier to validate and interpret. We demonstrate the method using the case of labeled party platforms (source corpus) and unlabeled parliamentary speeches (target corpus). In addition to the standard within-domain error metrics, we further validate the cross-domain performance by labeling a subset of target-corpus documents. We find that the classifier accurately assigns topics in the parliamentary speeches, although accuracy varies substantially by topic. We also propose tools diagnosing cross-domain classification. To illustrate the usefulness of the method, we present two case studies on how electoral rules and the gender of parliamentarians influence the choice of speech topics.