Empowering women facing gender-based violence amid COVID-19 through media campaigns

COVID-19 heightened women's exposure to gender-based and intimate partner violence, especially in low-income and middle-income countries. We tested whether edutainment interventions shown to successfully combat gender-based and intimate partner violence when delivered in person can be effectively delivered using social (WhatsApp and Facebook) and traditional (TV) media. To do so, we randomized the mode of implementation of an intervention conducted by an Egyptian women's rights organization seeking to support women amid COVID-19 social distancing. We found WhatsApp to be more effective in delivering the intervention than Facebook but no credible evidence of differences across outcomes between social media and TV dissemination. Our findings show little credible evidence that these campaigns affected women's attitudes towards gender or marital equality or on the justifiability of violence. However, the campaign did increase women's knowledge, hypothetical use and reported use of available resources.

An application of the dual identity model and active categorization to increase intercultural closeness

The enhancement of social inclusion is a key to maintaining cohesion in society and to foster the benefits of cultural diversity. Using insights from the Dual Identity Model (DIM) with a special focus on active categorization, we develop an intervention to increase social inclusion. Our intervention encourages the participants to (re-)categorize on a superordinate level (i.e., a human identity) while being exposed to their own culture. Across a set of experiments, we test the efficacy of our intervention against control conditions on the effect of social inclusion, measured by perceived social distance. Results show an increase in cultural closeness and provide preliminary support for the use of a DIM-based intervention to increase intercultural inclusion

Polling India via regression and post-stratification of non-probability online samples

Recent technological advances have facilitated the collection of large-scale administrative data and the online surveying of the Indian population. Building on these we propose a strategy for more robust, frequent and transparent projections of the Indian vote during the campaign. We execute a modified MrP model of Indian vote preferences that proposes innovations to each of its three core components: stratification frame, training data, and a learner. For the post-stratification frame we propose a novel Data Integration approach that allows the simultaneous estimation of counts from multiple complementary sources, such as census tables and auxiliary surveys. For the training data we assemble panels of respondents from two unorthodox online populations: Amazon Mechanical Turks workers and Facebook users. And as a modeling tool, we replace the Bayesian multilevel regression learner with Random Forests. Our 2019 pre-election forecasts for the two largest Lok Sahba coalitions were very close to actual outcomes: we predicted 41.8% for the NDA, against an observed value of 45.0% and 30.8% for the UPA against an observed vote share of just under 31.3%. Our uniform-swing seat projection outperforms other pollsters-we had the lowest absolute error of 89 seats (along with a poll from 'Jan Ki Baat'); the lowest error on the NDA-UPA lead (a mere 8 seats), and we are the only pollster that can capture real-time preference shifts due to salient campaign events.

Populism, Nationalism, and Nationalist Populism

This article builds an original, analytical framework to understand one of the most important developments of our times - the global ascendance of leaders who fuse populist anti-elite rhetoric with nationalist appeals. In contrast to arguments that treat populism and nationalism as either completely separate or essentially equivalent phenomena, I begin from an understanding of the two as distinct ideologies that grow from a shared foundational claim to represent an "us" versus a "them." In part 1, I first juxtapose populism and nationalism around this common, undergirding us-them boundary to bring out their core features. I then analyze how populism and nationalism vary across the twin axes of intensity and inclusiveness to bring out their distinct sub-types. In part 2, I use this theoretical map of populism and nationalism to navigate the conceptual terrain of their intersection. I focus, in particular, on the implications of nationalist populism for those seen as "us" versus those viewed as "them," where the "us" and "them" are determined by the dimensions of intensity and inclusiveness. In contrast to characterizations of nationalist populism in directional terms as negative, I suggest that it is instead better understood as an amplifying force that exacerbates both the positive and negative consequences of populism. All else equal, relative to populism, those beyond nationalist populist boundaries are subject to heightened hostility and discrimination, while those within benefit from enhanced life opportunities.

Group-affirmation and trust in international relations: Evidence from Ukraine

How can states with a history of recent armed conflict trust one another? Distrust between Ukraine and Russia aggravates security fears and limits hopes for a meaningful resolution of the bloodiest armed conflict in Europe since 1994. Hostility levels have risen dramatically between the populations of Ukraine and Russia after the events of 2013–2015. Political psychology offers two competing approaches to increase trust between the publics of different countries: appealing to an overarching, common identity above the national level vs. affirming a sense of national identity. This project asks which of these approaches increases trust towards Russia among the Ukrainian public. The study employs a survey experiment (between-subjects design) to evaluate these competing claims. The survey is to be fielded by a reputable public opinion research firm, the Kiev International Institute of Sociology, based in Ukraine.