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American political activism has surged recently among young citizens, particularly among women and people of color. At the same time, record numbers of women and minority candidates have been running for office. Does seeing more diverse candidates in terms of age, gender, and race propel more interest in political engagement among Generation Z, particularly women? Using a survey experiment embedded in a nationally representative survey of Generation Z citizens, we present respondents with Democratic politicians who vary based on these three criteria. Women who identify strongly with their gender express greater political engagement when presented with any candidate who does not fit the stereotypical image of a politician (older, white, male). They are spurred not only by role models who represent them descriptively, but by all politicians belonging to historically marginalized groups. These effects, which are not specific to just Democratic women, provide insights that can inform engagement efforts targeting younger Americans.
Though avoiding blame is often a goal of elected officials, there are relatively few empirical examinations of how citizens assign blame during controversies. We are particularly interested in how this process works when an executive has been caught in a lie. Using two survey experiments, we examine whether subordinates can shield executives when they act as the face of a crisis. We first leverage a real-life situation involving the family separation crisis at the US–Mexico border in 2018. Respondents who read that Donald Trump falsely claimed he could not end the practice of family separation disapprove of his dishonesty. Yet this cost disappears when Trump’s then-Secretary of Homeland Security, Kirstjen Nielsen, is the primary official discussed in news stories. We then replicate these findings in a fictional scenario involving a city mayor, showing that the mayor is partially shielded from negative appraisals when the city manager lies on his behalf.
How do the costs of conflict influence public support for the use of force? Existing research finds that weapons that eliminate the possibility of military casualties, such as drones, increase popular support for engaging in conflict. We argue that this effect may be overstated because the choice of weapons technology is endogenous to conflict. Leaders may select to use drones in conflicts where the risk of harm to ground forces is especially high. To address this, we replicate and extend the research design of Walsh and Schulzke across three survey experiments. The key innovation in our experiments is that subjects are led to believe that the choice of attack type – drones or ground troops – is determined by weather conditions rather than strategic considerations. We find that support for military action does not differ across treatments in which subjects are told that the attack involves drone strikes or ground troops.
Using a national survey of Generation Z conducted in late May 2020, we measure attitudes about the impact of the coronavirus on personal health, financial and job concerns, views about shelter-in-place laws, and 2020 voting intentions. Gen Z women express greater health and economic concerns and support for shelter-in-place measures than their male counterparts, but this gender gap is largely mitigated by party and other covariates. Party also mediates the differences between young male and female voters concerning the influence of the coronavirus on their vote choice in 2020. Notably, women have significantly greater concern about the impact of COVID-19 on their personal financial situation, while Gen Z men express more concern about their personal health amid COVID-19 in more fully specified statistical models. This research contributes to the growing literature that examines not only the sorting effect of party on the gender gap but also how different identities—in this case, generation—can help explain the persistent political divides between men and women.
OBJECTIVES/SPECIFIC AIMS: Recent evidence from resting-state fMRI studies have shown that brain network connectivity is altered in patients with neurodegenerative disorders. However, few studies have examined the complete connectivity patterns of these well-reported RSNs using a whole brain approach and how they compare between dementias. Here, we used advanced connectomic approaches to examine the connectivity of RSNs in Alzheimer disease (AD), Frontotemporal dementia (FTD), and age-matched control participants. METHODS/STUDY POPULATION: In total, 44 participants [27 controls (66.4±7.6 years), 13 AD (68.5.63±13.9 years), 4 FTD (59.575±12.2 years)] from an ongoing study at Indiana University School of Medicine were used. Resting-state fMRI data was processed using an in-house pipeline modeled after Power et al. (2014). Images were parcellated into 278 regions of interest (ROI) based on Shen et al. (2013). Connectivity between each ROI pair was described by Pearson correlation coefficient. Brain regions were grouped into 7 canonical RSNs as described by Yeo et al. (2015). Pearson correlation values were then averaged across pairs of ROIs in each network and averaged across individuals in each group. These values were used to determine relative expression of FC in each RSN (intranetwork) and create RSN profiles for each group. RESULTS/ANTICIPATED RESULTS: Our findings support previous literature which shows that limbic networks are disrupted in FTLD participants compared with AD and age-matched controls. In addition, interactions between different RSNs was also examined and a significant difference between controls and AD subjects was found between FP and DMN RSNs. Similarly, previous literature has reported a disruption between executive (frontoparietal) network and default mode network in AD compared with controls. DISCUSSION/SIGNIFICANCE OF IMPACT: Our approach allows us to create profiles that could help compare intranetwork FC in different neurodegenerative diseases. Future work with expanded samples will help us to draw more substantial conclusions regarding differences, if any, in the connectivity patterns between RSNs in various neurodegenerative diseases.
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