Suchergebnisse

Wearing facial coverings has become a key element in the fight against COVID-19. However, deep partisan divisions have arisen over the adoption of face masks, with Democrats more supportive than Republicans in the United States. Among opponents, a common argument is that facial coverings serve to dehumanize the wearer. Using an experimental study, I find no evidence, using a nationally diverse US sample, that face masks are dehumanizing, whether worn by a White or Black person. In addition, I test for moderation by partisanship, which shows a lack of dehumanizing effects and provides some suggestive evidence that face masks can humanize the wearer, for Democrats, though these effects are small. Under no circumstances do I find evidence that face masks dehumanize the wearer, even among Republican respondents.

AbstractWhen the United States intervenes in foreign countries, the lives of both foreign combatants and foreign civilians are put at risk. I examine two rhetorical strategies, the use of sanitized and dehumanizing language that can influence the public's support of foreign intervention. In the context of foreign policy, sanitized language operates by obscuring casualties of war, while dehumanizing language operates by devaluing the lives of groups of individuals. Drawing on data from two experiments, I find that sanitized language operates through creating less of an emotional reaction toward casualties of war, which causes individuals to adopt more hawkish foreign policy attitudes. I find that dehumanizing language also leads to more hawkish foreign policy attitudes, but, contrary to expectations, does not lead to increased disgust or anger toward dehumanized groups.

AbstractDehumanizing language, or language used to describe human beings as non-human entities, is increasingly prevalent in political life. This dehumanization also occurs frequently in the world of sports. Sports and politics intersected notably in 2016, when Colin Kaepernick of the NFL's San Francisco 49ers started protesting the national anthem to raise awareness about police violence against African-Americans. Kaepernick's protests generated considerable vitriol towards him and other protesters, some of which was dehumanizing. In this study, I examine how dehumanizing language used against anthem protesters of different races influences political attitudes. Using experimental data, I find that, when a Black player protesting the national anthem is dehumanized, White citizens are considerably less supportive of the anthem protests and protesters. This effect does not persist when the dehumanized player is White.

ABSTRACTNoting the lack of "anti-man" bias research in the 2016 election, Zigerell (2019) argued that a relative lack of conservatives in political science can lead to bias in publications against political science research supporting conservative viewpoints. This article offers an alternative explanation for this lack of research: that this research produces null findings and therefore is subject to the "file-drawer problem," in which null effects are less likely to be published than positive effects. Using data from the 2016 American National Election Studies, I provide an illustrative example to support this claim and suggest some solutions.

Abstract
As the U.S. Government works to slow the spread of the novel coronavirus, messaging is important in getting individuals to comply with public health recommendations, especially as the response from the public seems to be polarized along partisan and ideological lines. Using a recent Centers for Disease Control recommendation of wearing facemasks, I use Regulatory Focus Theory to predict that conservatives will be more responsive to messages related to promotion, while liberals are more responsive to messages related to prevention. Using a pre-registered experimental design, I find no evidence that prevention messages influence attitudes toward mask wearing. Promotion messages, however, cause conservatives to become less supportive of mask wearing, in contrast to theoretical predictions. These findings suggest that, related to messaging about mask wearing, strong ideological differences do not emerge related to the focus of the message.

In recent years, ideological candidates for the U.S. House have become increasingly successful, to the point where their chances of being elected are indistinguishable from moderates. However, scholars have still not uncovered exactly why this is happening. Using survey data from the American National Election Studies, I find that voter-centric explanations of vote choice – a voter's partisanship, ideology, and presidential approval rating – have increasingly predicted their vote choice in U.S. House elections from 1980 to 2016. Using data on candidate ideology, I find that candidate ideology is an increasingly poor predictor of individual vote choice over time. Original experimental data supports these claims, finding only a small electoral advantage for moderates, compared to ideologues of their own party, and evidence suggesting that, at least among Democrats, ideological candidates are rated more favorably than moderates. Taken together, these results suggest that the increased electoral success of ideological candidates can be attributed to changes in voters' decision calculus, rather than structural or candidate-centric explanations.

Immigrants, as a group, are frequently described in ways, such as vermin or disease, that portray them as less than human. This type of dehumanizing language leads to negative emotional responses and negative attitudes toward the dehumanized group. This paper examines how the dehumanization of immigrants influences immigration policy attitudes. I use original experimental data to show that dehumanization leads to more negative immigration attitudes. I further find that these negative attitudes are mediated by the role of emotion. Dehumanization increases anger and disgust toward immigrants, which causes anti-immigrant sentiment.

How do the words we use to talk about politics influence political attitudes and evaluations? I focus specifically on negative affective language—words which individuals have preexisting negative reactions toward. Considering the Affect Infusion Model (AIM), processing style influences how individuals use affect when making decisions. The impact of affective language depends upon the complexity of the decision. In simpler processing tasks, individuals will use affect as a heuristic. This causes a misattribution of generalized negative affect onto a political target, leading to harsher evaluations. When a decision is complex, affective language influences how new information is stored in memory, along with improving information recall and abstract thinking. For those who are exposed to negative affective language, negative evaluations of politicians persist more strongly in memory, while these evaluations fade away when affect is used as a heuristic.

Dehumanizing language, language that compares human beings to animals or machines, is typically thought of in problematic cases, where it is designed to denigrate individuals or entire groups in society. But, this language can also be used to praise—describing an employee as a machine can be done to signify super-human characteristics. We find that positive dehumanizing language has no effect on evaluations of a public employee's competence, but do have an effect on evaluations of warmth. Contrary to expectations, we find no differences in these effects based on the gender of the employee.

AbstractBackgroundThe COVID‐19 pandemic has presented unique challenges across the world in getting citizens to change their behaviors in response to a public health crisis. In the United States, it appears that partisan differences in willingness to comply with these measures have emerged: Democrats are typically more supportive than Republicans in their stated support of and willingness to comply with these measures. However, actual behaviors are notoriously hard to accurately capture with survey items.ObjectiveTo determine the extent to which county‐level partisanship influences average willingness to stay at home, and how these effects are moderated by county level characteristics.MethodsWe use personal device (cell phone) data provided by SafeGraph, aggregated at the county‐level, to determine how county‐level partisanship is correlated with willigness to stay at home. We additionally test whether these effects are conditional upon the prevalence of COVID‐19 in the county, and the percentage of the county under 30 years old.ResultsWe find that county‐level partisanship predicts aggregate level compliance with social distancing behavior—citizens of counties that are more Republican spend more time away from home than Democratic counties. We find that the number of COVID‐19 cases in the county and the percentage of the county under the age of 30 moderate these effects.ConclusionPartisanship appears to be a powerful predictor, at the county‐level, of willigness to follow stay at home orders in the early stages of the COVID‐19 pandemic.