What are the chances you’re right about everything? An epistemic challenge for modern partisanship

The American political landscape exhibits significant polarization. People’s political beliefs cluster around two main camps. However, many of the issues with respect to which these two camps disagree seem to be rationally orthogonal. This feature raises an epistemic challenge for the political partisan. If she is justified in consistently adopting the party line, it must be true that her side is reliable on the issues that are the subject of disagreements. It would then follow that the other side is anti-reliable with respect to a host of orthogonal political issues. Yet, it is difficult to find a psychologically plausible explanation for why one side would get things reliably wrong with respect to a wide range of orthogonal issues. While this project’s empirical discussion focuses on the US context, the argument generalizes to any situation where political polarization exists on a sufficiently large number of orthogonal claims

A Data Fusion Framework for Multi-Domain Morality Learning

Language models can be trained to recognize the moral sentiment of text, creating new opportunities to study the role of morality in human life. As interest in language and morality has grown, several ground truth datasets with moral annotations have been released. However, these datasets vary in the method of data collection, domain, topics, instructions for annotators, etc. Simply aggregating such heterogeneous datasets during training can yield models that fail to generalize well. We describe a data fusion framework for training on multiple heterogeneous datasets that improve performance and generalizability. The model uses domain adversarial training to align the datasets in feature space and a weighted loss function to deal with label shift. We show that the proposed framework achieves state-of-the-art performance in different datasets compared to prior works in morality inference

Recentering Civics: A Framework for Building Civic Dispositions and Action Opportunities

Many civic education initiatives have developed across the United States in order to help prepare students for civic engagement in school-based settings. At the same time, research shows that quality of school-based civic learning opportunities remains insufficient, inconsistent, and inequitable. In this article, we propose a framework of civic learning dispositions based upon current social studies curricular resources from C3 Teachers. Based on a thematic review of civic dispositions embedded within this C3 Framework-aligned curriculum, we offer a framework to demonstrate how civic dispositions and the application of social studies learning (i.e., civic action) can be used in curriculum design to support a reinvigorated application of social studies learning. The framework, then, provides a theoretically informed, practical heuristic for teachers, researchers and curricular designers to both better understand and subsequently support their students’ high-quality civic learning in the context of social studies teaching and learning

A Cross-Cultural Content Analysis of the 2008 American Presidential Election

Agenda-setting theory, based on the idea that journalism has a strong correlation to audience opinion and evaluation of issue salience, shows the unportance of how the media present the news. Naturally, media values vary across cultures, wherein some news outlets serve only to inform and others approach news through partisan positions or evaluative tactics. Especially during a time of media upset in the United States, it is important to examine U.S. media values and those of other cultures. This thesis looks at some of the dominant and documented differences in newspaper reporting in France and the United States, using Patterson’s governing and game schema as a basis. In accordance with previous research, French journalism was found to be more evaluative and American journalism to employ a game schema more often, although the results are mainly inconclusive due to the small sample size and large margins of error