Introducing an international relations perspective into the literature on anti-immigrant attitudes, we hypothesize that immigrants from rival countries will be shunned and immigrants from allied countries preferred, especially by respondents who identify more strongly with the nation. We fielded a forced-choice conjoint experiment in 22 countries, whereby respondents chose between applicants for permanent resident status with randomized attributes. We identified rival and allied countries of origin for each surveyed country, with one such pair sharing a similar racial and cultural make-up as the majority of respondents, and one pair being more dissimilar. We find that discrimination against immigrants from rival states is so pronounced that it results in a net preference for racially and culturally dissimilar immigrants. Since we fielded the surveys amidst the Russian invasion of Ukraine, we are able to leverage exogenous changes in the intensity of one rivalry, providing further evidence for the proposed mechanism.

This study reflects on the field research interruptions that occurred around the world with the onset of the COVID-19 pandemic. Based on my experience of in-person and remote fieldwork with vulnerable populations and sensitive research topics during this time, I introduce a “zig-zagging approach” that can be used as a research adaptivity strategy in times of disruption. I argue that “zig-zagging your way through the field” is a legitimate strategy as long as researchers acknowledge that changing from in-person to remote fieldwork (and vice versa) will alter various aspects of their relationship with the field including;(1) perception of positionality and authenticity; (2) processes of trust building and security challenges; and (3) experience of ethnographic immersion and observation. I offer mitigation strategies to reduce the impact of change and also discuss aspects that cannot be mitigated when working with vulnerable populations or sensitive research topics. I conclude on why going back—and forth (i.e., zig-zagging)—should become a practical solution when all else fails.

Plea bargaining figures heavily in criminal justice systems in the United States and, increasingly, around the globe. Conventional wisdom holds that plea bargaining generates efficiency gains for all parties, while sorting the guilty from the innocent. We build a series of formal models to consider the relationship between a defendant’s guilt and her likelihood of pleading guilty. In an inversion of the conventional wisdom, we show that under a range of empirically plausible scenarios—for example, if criminals are more risk-seeking than the wrongfully accused, or if prosecutors derive a career benefit from trial wins—the innocent are more likely than the guilty to plea bargain.

A long-standing debate in political psychology considers whether individuals update their beliefs and attitudes in the direction of evidence or grow more confident in their convictions when confronted with counter-attitudinal arguments. Though recent studies have shown that instances of the latter tendency, which scholars have termed attitude polarization and “belief backfire,” are rarely observed in settings involving hot-button issues or viral misinformation, we know surprisingly little about how participants respond to information targeting deeply held attitudes, a key condition for triggering attitude polarization. We develop a tailored experimental design that measures participants’ core issue positions and exposes them to personalized counter-attitudinal information using the large language model GPT-3. We find credible evidence of attitude polarization, but only when arguments are contentious and vitriolic. For lower valence counter-attitudinal arguments, attitude polarization is not detected. We conclude by discussing implications for the study of political cognition and the measurement of attitudes.

Social scientists often classify text documents to use the resulting labels as an outcome or a predictor in empirical research. Automated text classification has become a standard tool since it requires less human coding. However, scholars still need many human-labeled documents for training. To reduce labeling costs, we propose a new algorithm for text classification that combines a probabilistic model with active learning. The probabilistic model uses both labeled and unlabeled data, and active learning concentrates labeling efforts on difficult documents to classify. Our validation study shows that with few labeled data, the classification performance of our algorithm is comparable to state-of-the-art methods at a fraction of the computational cost. We replicate the results of two published articles with only a small fraction of the original labeled data used in those studies and provide open-source software to implement our method.

This article presents Latinx political thought as a distinctive tradition in political theory that reworks central concepts in response to historical experiences of conquest, colonialism, migration, and transnational politics. In reconstructing this tradition, we argue that its problem space converges with US-based Latin American political thought. The article first traces a genealogy of Latinx political theory and then explores three realms of theorizing around which Latinx and Latin American political thought cluster: sovereignty and state violence, peoplehood, and transnationalism. We explain how the surveyed works disrupt and enrich political theory accounts of these problems. In arguing for the recognition of this field as a tradition, the article also aims to make it intelligible as an area of concentration for PhD students in political science.

I draw together theories of partisan polarization and motivated reasoning, which suggest that partisanship shapes information processing, and theories of accountability, which argue voters hold elected officials accountable through promise fulfillment. Here, I ask how partisanship influences voter understanding of promise fulfillment and accountability and if voters assess promises through a partisan lens. Two original survey experiments test how respondents react to promise fulfillment on the issues of immigration and human trafficking. I demonstrate that co-partisans differentiate between kept and broken promises, but out-partisans do not. Despite partisan differences, respondents evaluate promise-keeping when asked about accountability but not when asked about approval. Thus, even when voters recognize broken promises, accountability is influenced by partisanship. Immigration, a more polarized issue, is more likely to prime a partisan response than human trafficking, a less polarized issue. Future work must account for partisanship in accountability and what this means for our understanding of fundamental democratic principles.

To make a valid will, a person should be able to understand the nature and consequences of doing so, the extent of their estate and the claims others may have on it. No disorder of mind should be present that would affect their testamentary decisions, and clinicians are therefore often asked to give an opinion on whether a person has testamentary capacity. This article discusses the legal issues involved, with reference to UK case law (in particular, the legal test of Banks v Goodfellow (1870)), and outlines the requirements of testamentary capacity assessment (including retrospective assessments), the clinician's responsibilities when requested by a solicitor to make an assessment of capacity (‘the golden rule’) and what they might expect if appearing in court to give expert witness regarding testamentary capacity. Fictitious case studies are presented illustrating certain points in testamentary capacity assessment.

Vigilante violence, often targeting religious and sectarian minorities and preceded by unsubstantiated rumors, has taken the lives of many citizens in India and Pakistan in recent years. Despite its horrific nature, such vigilantism receives popular support. Can reducing the credibility of rumors via corrections decrease support for vigilantism? To answer this question, we field simultaneous, in-person experiments in Punjab, Pakistan, and Uttar Pradesh, India, regions where anti-minority vigilantism has been preceded by misinformation. We find that correcting rumors reduces support for vigilantism and increases the desire to hold vigilantes accountable. This effect is not attenuated by prior distrust toward out-groups. By contrast, information about state and elite behavior does not always shape attitudes toward vigilantism. These findings provide evidence that support for vigilantism can be reduced through the dissemination of credible information, even in polarized settings.

American voters consume an astounding amount of entertainment media, yet its political consequences are often neglected. We argue that this ostensibly apolitical content can create unique opportunities for politicians to build parasocial ties with voters. We study this question in the context of Donald Trump’s unconventional political trajectory and investigate the electoral consequences of The Apprentice. Using an array of data—content analysis, surveys, Twitter data, open-ended answers—we investigate how this TV program helped Trump brand himself as a competent leader and foster viewers’ trust in him. Exploiting the geographic variation in NBC channel inertia, we find that exposure to The Apprentice increased Donald Trump’s electoral performance in the 2016 Republican primary. We discuss the implications of these findings in light of the rise of nonconventional politicians in this golden age of entertainment.

Although commentators often point to the political value of legislators’ geographic ties, less is known about the influence of such connections once in office. Given recent scholarship underscoring the importance of geography as a dimension of identity, we argue that local legislators should behave as descriptive representatives. We collect the hometowns of all members of Congress with known birth locations from 1789 to 2020 to analyze how being born near one’s district impacts legislator behavior. We connect these data to information on a series of behaviors, finding that local legislators emphasize constituency work over policymaking and party-building. Moreover, while local legislators do not demonstrate substantively less partisan unity in roll-call voting, they attract a higher percentage of out-party cosponsors to their bills. Together, our results point to important representational implications regarding the geographic roots of legislators and the role of local connections in the contemporary Congress.

Political leaders increasingly use social media to speak directly to voters, but the extent to which elite cues shape offline political behavior remains unclear. In this article, we study the effects of elite cues on noncompliant behavior, focusing on a series of controversial tweets sent by US President Donald Trump calling for the “liberation” of Minnesota, Virginia, and Michigan from state and local government COVID-19 restrictions. Leveraging the fact that Trump’s messages exclusively referred to three specific US states, we adopt a generalized difference-in-differences design relying on spatial variation to identify the causal effects of the targeted cues. Our analysis shows that the President’s messages led to an increase in movement, a decrease in adherence to stay-at-home restrictions, and an increase in arrests of white Americans for crimes related to civil disobedience and rebellion. These findings demonstrate the consequences of elite cues in polarized environments.