Oct 23, 2019 | Current Events, Elections, National, Policy, Race, Social Policy
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 the 2019 Hanes Walton, Jr. lecture.
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 the Plessy 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 of Hanes 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.
Sep 26, 2019 | Current Events, Elections, Innovative Methodology, National
Post developed by Katherine Pearson
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.
The judges agreed with Chen’s analysis that the 2017 maps displayed Republican bias, compared to the maps he generated by computer that left out partisan and racial considerations. On September 3, 2019, the state court struck down the maps as unconstitutional and enjoined their use in future elections.
The North Carolina General Assembly rushed to adopt new district maps by the court’s deadline of September 19, 2019. To simplify the process, legislators agreed to use Chen’s computer-simulated maps as a starting point for the new districts. The legislature even selected randomly from among Chen’s simulated maps in an effort to avoid possible accusations of political bias in its new redistricting process.
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.
Sep 6, 2019 | APSA, Current Events, National, Policy, Race, Social Policy
Post developed by Nicholas Valentino, Ali Valenzuela, Omar Wasow, 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 “Rousing the Sleeping Giant? Emotions and Latino Mobilization in an Anti-Immigration Era” was a part of the session “The Rhetoric of Race” on Friday, August 30, 2019.
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.
Sep 1, 2019 | APSA, Current Events, International
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.
Aug 30, 2019 | APSA, Current Events, Elections, Innovative Methodology
Post developed by Dory Knight-Ingram
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 “Using Neural Networks to Classify Based on Combined Text and Image Content: An Application to Election Incident Observation” was a part of the session “Deep Learning in Political Science” on Friday, August 30, 2019.
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.”
Aug 30, 2019 | APSA, Current Events, Policy
Post developed by Erin Cikanek, Nicholas Valentino, 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 “The Politicization of Policies to Address Climate Change” was a part of the session “The Dynamics of Climate Policy Support in the US” on Friday, August 30, 2019.
Climate change is a truly polarizing issue. Partisans on either side of the issue have such deeply entrenched beliefs that there is little that can change minds. But this wasn’t always the case. For example, in 1988 Democrats and Republicans were in close agreement about the amount of money the government should spend on environmental protection. More recently, partisans have become more polarized in their level of concern about climate change.
How do scientific policy issues become so polarized, and how quickly does this happen? New research by Nicholas Valentino and Erin Cikanek measures public awareness of Carbon Dioxide Removal (CDR) polices, and explores whether attitudes toward these policies are as politicized as climate change overall.
Valentino and Cikanek conducted two studies to examine political polarization of CDR. First, they surveyed a large, nationally representative sample of people to measure how much they knew about climate change. The questions covered a broad set of issues and strategies for dealing with the problem. This survey revealed that the public has a high level of knowledge about climate change.
The study also demonstrates that partisanship is highly predictive of knowledge about climate change. Democrats responded with significantly more accuracy than Republicans did. When respondents were asked specifically about technologies to remove carbon dioxide from the atmosphere, overall knowledge was lower, but Democrats and Republicans answered questions with the same level of accuracy, as shown in the figure below. Valentino and Cikanek note that “this pattern is consistent with the possibility that elite rhetoric has come to suppress accuracy on general climate change knowledge among Republicans, but this has not yet occurred for knowledge in this newer domain (CDR).”
The second study experimentally tested whether CDR policies are sensitive to partisan cues. CDR policies have not been debated as much or as publicly as climate change in general. Are these policies as susceptible to political polarization?
Survey respondents were randomly assigned to one of three groups. A control group was asked about current climate change policies, as well as carbon reduction policies. The first treatment group received information that applied partisan stereotypes to the CDR policies: Republican hesitation about CDR because it might hinder business, and Democratic encouragement to save the environment. The second treatment group received information that ran counter to those stereotypes: Republican support for a pro-business solution to climate change, and Democratic concern that the solution may encourage businesses to pollute.
The partisan cues had very little effect on the response to CDR policies. Interestingly, the counter-intuitive partisan cues backfired: when Republican respondents read the treatment showing Republican support for CDR, they opposed it slightly more. The very weak effect of partisan cues on support for CDR may show that CDR policies may be more resistant to polarization.
The more politicians discuss scientific policy issues, the more polarized the discussion tends to become. However, Valentino and Cikanek see reason to hope that compromises remain possible for issues like carbon removal, which have not yet been subjected to partisan rhetoric.
