“Racial animus is in the air we breathe,” Streeter says, “but when we look at police violence, we can get distracted by race and ignore other important factors.” Her dissertation included an experiment to examine how the race of victims of police violence determines whether the public sees the violence as just. Surprisingly, she finds that the race of the victims is less salient than expected. Instead, the social context strongly shaped the attitudes of the respondents. Those who were predisposed to consider societal and institutional forces were less likely to believe the victim deserved the outcome, compared to respondents who place sole responsibility on the individual.
Racial differences in rates of protest
Half of the people killed by police each year are white, and yet the rate of protest over white victims of police violence is very low. A dataset that Streeter is currently completing includes all publicly available information on police killings and any protests that happened in 2015-2016. For those two years, about a third of the police killings of African Americans led to some sort of protest, but when whites were killed by police, protests occurred only five percent of the time. “I argue that it’s the biggest racial gap related to policing,” Streeter says. “There are a lot of reasons we could point to why African Americans would be protesting. But why wouldn’t whites also be protesting when their community members are killed?”
When conducting field research in several different cities in the United States, Streeter asked community organizers about protests for white victims of police violence. The organizers told her that they reach out to the families of white victims, but those families often do not want to be involved with protests. Instead, many white family members express understanding and forgiveness toward the police. Streeter makes sense of these reactions by tying them to the psychological concept of a belief in a just world. The idea is that people get what they deserve and they deserve what they get. Streeter observes that even when people who hold this belief lose a member of their own family, their trust in the police remains unchanged. “If you have these beliefs, it can be like a double loss,” Streeter notes, which may explain why there are fewer protests for white victims of police violence.
The role of mentorship
Mentorship has played a large role in Streeter’s academic career. Christian Davenport became a mentor to her when she was a senior at Notre Dame. At that time, Streeter was thinking about her career but hadn’t considered pursuing research. While working as a research assistant for Davenport, he encouraged her to pursue graduate work in political science. Streeter cites this support as a key reason she decided to come to the University of Michigan. She also gives credit to David Laitin and Jeremy Weinstein at Stanford, who pushed her to study the United States when she was training as a comparativist. “I had confusion about what my identity as a scholar would be if I changed paths, but they put my fears to rest, so I give them a lot of credit for helping me pursue this research path,” Streeter says.
Looking forward
In addition to her ongoing research on police violence, Streeter is turning her attention to the ways interpersonal violence affects the way that people think and act politically. She sees connections between different types of violence, including mass shootings, domestic violence, and suicide. “We don’t often see these as political violence, but they affect how people operate in the world,” Streeter says. She’s especially interested in the ways violence affects people differently based on gender. Streeter’s work is innovative and varied, but united by a common theme, which she sums up as “How does violence affect our world, and what are the aggregate consequences of that? That’s the big picture.”
Post developed by Katherine Pearson and Dianne Pinderhughes
Drawing from published work that will be compiled as a new book, Black Politics After the Civil Rights Revolution, Dianne Pinderhughes explored the arc of 20th-century civil rights reform and the growing political incorporation of African Americans into electoral politics when she delivered the2019 Hanes Walton, Jr. lecture. A recording of the lecture is available below.
Understanding the history of collective action is essential to tracing the development of 20th-century racial politics in the United States. Pinderhughes began by describing racial injustice in the U.S. starting with thePlessy v. Ferguson decision in 1896, which some consider the nadir of race relations in the U.S. Following this era, Pinderhughes described a period of innovation and institution building beginning in the early and mid 20th century, which saw the development of legal defense funds and an increase of racial diversity in academia.
Social and political scientists recognize the gradual increase in African American political participation and the increasing numbers of elected officials of color. As the political dynamics of the eras changed, Pinderhughes described how African Americans have pushed to enter, to change, and to reframe their status.
Pinderhughes posits that the election of Donald Trump in 2016 posed a direct challenge to that framing of the evolution of successful racial reform. In doing so, she asks whether the U.S. is entering a new nadir. “My own work around these issues of democracy, political participation and efforts to integrate on a stable basis, and to begin to address the economic and political dimensions of citizenship, was challenged by how they might be framed,” Pinderhughes said. “But most of that work began from and was conceptualized within a relatively stable set of policy values and expectations, and that racial and ethnic exclusion was no longer possible, or acceptable.”
In the end, Pinderhughes concludes that the state of politics in the 21st century is far more hopeful than the nadir of the 19th and early 20th centuries. Institutional reforms have substantially recreated the American electoral and political process. Race is central to American life, and it will continue to be a dynamic force in electoral politics.
The Hanes Walton, Jr. lecture series was launched in 2015, in honor ofHanes Walton, Jr. One of the most influential and productive political scientists to emerge from the civil rights era, Walton published numerous journal articles, several book chapters, and authored more than twenty books. Walton is remembered for his in-depth subject knowledge, sense of humor, and ability to connect with his students. He was a caring and supportive mentor to his countless graduate and undergraduate students, many of whom have gone on to distinguished careers in academia and industry.
How much does partisanship explain how legislative districts are drawn? Legislators commonly agree on neutral criteria for drawing district lines, but the extent to which partisan considerations overshadow these neutral criteria is often the subject of intense controversy.
Jowei Chen developed a new way to analyze legislative districts and determine whether they have been unfairly gerrymandered for partisan reasons. Chen, an Associate Professor of Political Science and a Research Associate at the Center for Political Studies, used computer simulations to produce thousands of non-partisan districting plans that follow traditional districting criteria.
These simulated district maps formed the basis of Chen’s recent expert court testimony in Common Cause v. Lewis, a case in which plaintiffs argued that North Carolina state legislative district maps drawn in 2017 were unconstitutionally gerrymandered. By comparing the non-partisan simulated maps to the existing districts, Chen was able to show that the 2017 districts “cannot be explained by North Carolina’s political geography.”
The simulated maps ignored all partisan and racial considerations. North Carolina’s General Assembly adopted several traditional districting criteria for drawing districts, and Chen’s simulations followed only these neutral criteria, including: equalizing population, maximizing geographic compactness, and preserving political subdivisions such as county, municipal, and precinct boundaries. By holding constant all of these traditional redistricting criteria, Chen determined that the 2017 district maps could not be explained by factors other than the intentional pursuit of partisan advantage.
Specifically, when compared to the simulated maps, Chen found that the 2017 districts split far more precincts and municipalities than was reasonably necessary, and were significantly less geographically compact than the simulations.
By disregarding these traditional standards, the 2017 House Plan was able to create 78 Republican-leaning districts out of 120 total; the Senate Plan created 32 Republican-leaning districts out of 50.
Using data from 10 recent elections in North Carolina, Chen compared the partisan leanings of the simulated districts to the actual ones. Every one of the simulated maps based on traditional criteria created fewer Republican-leaning districts. In fact, the 2017 House and Senate plans were extreme statistical outliers, demonstrating that partisanship predominated over the traditional criteria in those plans.
Determining whether legislative maps are fair will be an ongoing process involving courts and voters across different states. But in recent years, the simulation techniques developed by Chen have been repeatedly cited and relied upon by state and federal courts in Pennsylvania, Michigan, and elsewhere as a more scientific method for measuring how much districting maps are gerrymandered for partisan gain.
Since the 2016 presidential campaign anti-immigration policies have been very popular among President Trump’s strongest supporters, though they do not present obvious benefits to the economy or national security. Strategists suppose that the intent of the anti-immigration rhetoric and policies is to energize the president’s base.
But what about people who identify with the targets of these policies, specifically Latinos? Are they mobilized against anti-immigration proposals, or are they further deterred from political participation?
New research by Nicholas A. Valentino, Ali Valenzuela, and Omar Wasow finds that anger was associated with higher voter turnout among Latinos, but the Latinos who expressed more fear had lower voting rates.
The role of emotions in politics is complex. The research team begins with the observation that negative emotions do not always have negative consequences for politics. Indeed, negative emotions may promote attention and interest, and drive people to vote. They draw a distinction between different negative emotions: while anger may spur political action, fear can suppress it.
The research team fielded a nationally-representative panel survey of white and Latino registered voters before and after the 2018 midterm elections. Respondents were asked about their experience with Immigration and Customs Enforcement (ICE) officials and their exposure to campaign ads focused on immigration. Participants were also asked to rate their emotional reactions to the current direction of the country.
The results showed that Latinos interacted with ICE more frequently than whites did, but both groups had the same level of exposure to campaign ads. Latinos reported more anger than whites, and also more fear. In fact, among the negative emotions in the survey, fear among Latinos was highest.
In the sample the validated voting rate among Latinos was 39%; among whites in the sample it was 72%, demonstrating the under-mobilization of Latino voters. Whether Latinos vote in greater numbers in 2020 may depend on whether they are mobilized by anger against anti-immigration rhetoric, or whether they are deterred by fear stemming from policies like ICE detention and deportation.
Post developed by Pauline Jones, Anil Menon, and Katherine Pearson
ICYMI (In Case You Missed It), the following work was presented at the 2019 Annual Meeting of the American Political Science Association (APSA). The presentation, titled “Putin’s Pivot to Populism” was a part of the session “Russia and Populism” on Sunday, September 1, 2019.
The rise in populism around the world has received much attention, but not all populists are the same. In a new paper, Pauline Jones and Anil Menon present an original typology of populists that goes beyond typical left-wing versus right-wing classifications.
To better understand the different types of populists and how they operate, Jones and Menon examine two key dimensions: position within the political landscape (outsider versus insider), and level of ideological commitment (true believer versus opportunist).
Populists tend to frame their criticism of political elites differently depending on whether they are political outsiders or government insiders. While outsiders are free to criticize those in power broadly, populists who hold political power are more likely to tailor their criticisms to their political opponents. Insiders are also more careful not to attack members of the elite with whom they will need to build political coalitions.
Many populists evoke the past, but outsiders and insiders tend to do so differently. Whereas outsiders focus on the near past as a critique of a corrupt elite, political insiders instead focus on the distant past to evoke better days of shared national values.
Jones and Menon also draw distinctions between true believers in populism and those who embrace populism for purely strategic reasons. True believers will remain strongly committed to enacting their populist agenda once in office; opportunists will use populist rhetoric to gain power, but won’t support their platform strongly if elected.
The intersection of these two dimensions leads to the classification of populists into four types, illustrated in the table below: Oppositional, Classical, Strategic, and Pivot.
The most common variety of populist is the oppositional populist, who are outsiders and true believers. Oppositional populists put their agenda before all else and distance themselves from the mainstream elite.
Classical populists sometimes start out as outsiders who become insiders once they are elected to office. Like oppositional populists, they are strongly committed to enacting their agenda; unlike oppositional populists, classical populists can enact their agenda from a position of power. Because they are insiders, classical populists are more selective about criticizing elites.
Pivot populists are a rare group of political insiders who adopt populist rhetoric with little or no commitment to the populist ideology. Jones and Menon point to Russia’s Vladimir Putin as an example of a pivot populist who has adopted populism to bolster support for his regime while deflecting blame for the country’s problems.
The final category is strategic populists. Like Donald Trump in the United States, strategic populists are outsiders with a weak commitment to the populist agenda. Strategic populists are broadly anti-elite, and also use their rhetoric to create divisions among the people. Once in power, they are unlikely to alienate elites by pursuing populist policy goals.
A new election forensics process developed by Walter Mebane and Alejandro Pineda uses machine-learning to examine not just text, but images, too, for Twitter posts that are considered reports of “incidents” from the 2016 US Presidential Election.
Mebane and Pineda show how to combine text and images into a single supervised learner for prediction in US politics using a multi-layer perceptron. The paper notes that in election forensics, polls are useful, but social media data may offer more extensive and granular coverage.
The research team gathered individual observation data from Twitter in the months leading up to the 2016 US Presidential Election. Between Oct. 1-Nov. 8, 2016, the team used Twitter APIs to collect millions of tweets, arriving at more than 315,180 tweets that apparently reported one or more election “incidents” – an individual’s report of their personal experience with some aspect of the election process.
At first, the research team used only text associated with tweets. But the researchers note that sometimes, images in a tweet are informative, while the text is not. It’s possible for the text alone to not make a tweet a report of an election incident, while the image may indeed show an incident.
To solve this problem, the research team implemented some “deep neural network classifier methods that use both text and images associated with tweets. The network is constructed such that its text-focused parts learn from the image inputs, and its image-focused parts learn from the text inputs. Using such a dual-mode classifier ought to improve performance. In principle our architecture should improve performance classifying tweets that do not include images as well as tweets that do,” they wrote.
“Automating analysis for digital content proves difficult because the form of data takes so many different shapes. This paper offers a solution: a method for the automated classification of multi-modal content.” The research team’s model “takes image and text as input and outputs a single classification decision for each tweet – two inputs, one output.”
The paper describes in detail how the research team processed and analyzed tweet-images, which included loading image files in batches, restricting image types to .jpeg or .png., and using small image sizes for better data processing results.
The results were mixed.
The researchers trained two models using a sample of 1,278 tweets. One model combined text and images, the other focused only on text. In the text-only model, accuracy steadily increases until it achieves top accuracy at 99%. “Such high performance is testimony to the power of transfer learning,” the authors wrote.
However, the team was surprised that including the images substantially worsened performance. “Our proof-of-concept combined classifier works. But the model structure and hyperparameter details need to be adjusted to enhance performance. And it’s time to mobilize hardware superior to what we’ve used for this paper. New issues will arise as we do that.”
