Despite significant debate about the ability of international law to constrain state behavior, recent research points to domestic mechanisms that deter non-compliance, most notably public disapproval of governments that violate treaty agreements. However, existing studies have not explicitly differentiated two distinct, theoretically important motivations that underlie this disapproval: respect for legal obligations versus the desire to follow common global practices. We design an innovative survey experiment in Japan that manipulates information about these two potential channels directly. We examine attitudes towards four controversial practices that fall afoul of international law—same-surname marriage, whaling, hate speech regulation, and capital punishment—and find that the legal obligation cue has a stronger effect on respondent attitudes than the common practices cue. We also show subgroup differences based on partisanship and identification with global civil society. These results demonstrate that the legal nature of international law is crucial to domestic compliance pull.

No abstract

When using text data, social scientists often classify documents in order to use the resulting document labels as an outcome or predictor. Since it is prohibitively costly to label a large number of documents manually, automated text classification has become a standard tool. However, current approaches for text classification do not take advantage of all the data at one's disposal. We propose a fast new model for text classification that combines information from both labeled and unlabeled data with an active learning component, where a human iteratively labels documents that the algorithm is least certain about. Using text data from Wikipedia discussion pages, BBC News articles, historical US Supreme Court opinions, and human rights abuse allegations, we show that by introducing information about the structure of unlabeled data and iteratively labeling uncertain documents, our model improves performance relative to classifiers that (a) only use information from labeled data and (b) randomly decide which documents to label at the cost of manually labelling a small number of documents.