Transportation infrastructure like freeways provides an excellent lens through which to look at the issue of environmental justice. There is legislation in place that should prevent or at least draw significant attention to environmental justice, yet new freeways are still being proposed which continue to worsen environmental justices as decades past. With grassroots opposition as a primary form of resistance, this paper investigates the tools available to activists, as well as the ones most effective in ensuring the success of the movement. This paper is based upon three case studies of recent freeway proposals across the United States – the successful resistance to the Harbor Bridge Project in Corpus Christi, Texas; the unsuccessful resistance to the Central 70 Project in Denver, Colorado; and the unsuccessful resistance to the South Mountain Freeway Project in Phoenix, Arizona – as well as ten open-ended interviews with community organizers, government officials, and lawyers involved with the projects’ resistance movements in some way. Through this process, I found that two main factors determine the success of a resistance movement: organization (the timing and coordination of the resistance) and opportunity (the local and federal context and framing of the project and resistance). Through the interviews and in-depth analysis of formal government documents and news articles, I conclude that while aspects of a group’s organizational capacity were important to the outcome, the political opportunity available to them – specifically the attitudes and priorities of the current federal administration – was most critical to the success of a movement. While this leaves several questions for the future of environmental justice resistance, I end with a series of suggestions for how government officials and grassroots organizers might approach the issue with the current federal administration.

Since the 1920s, plea bargaining in the United States criminal justice system has become status quo. Today, over 95% of federal cases have resulted in plea bargains and never been tried in court. The power of the prosecutor within the legal system has also increased substantially over the last century leading many scholars to believe that the proliferation of plea bargaining is a direct result of heightened prosecutorial power. Prior literature has addressed the increased power of the prosecutor and the need for reform if plea bargaining rates are to change, but has not yet provided an empirical observation as to whether prosecutorial reform makes a significant impact in plea bargaining. In this paper I theorize that the prosecutor’s discretion over exculpatory evidence plays an integral part in states’ reliance on plea bargaining and that states that attempt to reign in prosecutorial power experience reduced rates of pleas. To test this theory systematically, I have run a multivariate analysis at the county-level to compare rates of plea bargaining across states that have reformed prosecutorial discretion and states that have not. If states with a model rule suppressing prosecutorial power show a statistically significant difference in plea rates, criminal justice reform advocates should turn their attention to prosecutorial reform.

One of the most common questions that scholars of democratic theory address is how to best allocate and balance power across the different structures within a government. Historically, many theorists and philosophers have postulated that structures with separation of power are more effective at resisting tyrannical rule. Though researchers have established the effects of an imbalance of power between two branches of government, no study has attempted to systematically account for the relative balance of power among all three branches working in tandem or develop empirical metrics to this end. In this study, I theorize that designing separate branches of government that are equally strong strengthens conflicts across institutions, which ultimately leads to more robust protections against tyranny. I evaluate this model quantitatively by developing and introducing a new measure, the Separate Powers Index (SPI). My SPI assesses the balance of power between the three branches of government as postulated in a sample of 130 of the world’s constitutions. Using multivariate regression methods, I compare the SPI with a cross-national index of free expression in a cross-sectional analysis during the year 2008, I can systematically examine whether there is a relationship between structural provisions of institutions that distribute power and their outcomes to protect their citizens. In addition to providing a novel measure of tripartite power balance in national constitutions, the result of this study has a large impact on all scholars of constitutionalism and civil liberties.

Scholars recognize that politicians’ perceptions of their electability influence their home style, or the way in which they present themselves to their constituents. Marginality, or the proportion of a politician’s co-partisans in the electorate, is common indicator of electability. However, marginality fails to capture how polarization of the electorate augments politicians’ vulnerability. This study introduces a new indicator of electability that captures statewide polarization in the electorate. Building off the finding that more marginal Senators emphasize support for appropriations to build non-partisan support, I expect that Senators in more polarized states will emphasize their support for appropriations after controlling for marginality. Appropriations credit-claiming builds non-partisan support without alienating more partisan voters. I employ multilevel linear regression analysis to examine the relationship between state-level partisan polarization and topic expression in Senate press releases systematically. A positive relationship between partisan polarization and appropriations credit-claiming may reveal a mechanism by which polarization paradoxically minimizes the partisan content of Senators’ home styles.

For this project, I examine why the majority of white women voted for Trump in the 2016 election, a fact that came as a shock to many observers. However, with few exceptions, white women have been shown to support conservative candidates in presidential elections since the 1950s. Yet, Donald Trump’s election can be considered unique as he was still able to gain most white women’s support despite his displays of various behaviors that could be regarded as overtly sexist. In this research design, I analyze white female voters using data from a 2016 Pilot Study by the American National Election Survey and hypothesize that racial resentment and internalized sexism are the primary factors that drove white women to support Donald Trump, controlling for partisanship, economic anxiety and other factors that might influence vote choice. I employ linear regression models using R programming software to examine a relationship between 2016 vote choice, racial resentment and internalized sexism systematically. In doing so, my analysis takes an intersectional approach, where both race and gender dynamics are useful in providing an explanation for white women’s support for Trump.

Although legislative partisanship has traditionally been studied through measures like floor votes or debates, scholars have found that social media also provides a conducive environment for negative and positive displays of party politics. This project codes Tweets from both Washington and Texas State legislators in 2017 as either “partisan” or “neutral” in order to create a proportion for each category and correspondingly identify the extent to which these legislators participate in partisan behavior online. Then, these proportions are compared with an individual legislator’s roll call votes, markers of their general level of polarization, in order to view whether or not they are behaving in a more partisan manner online than their votes would indicate. My paper posits that the unique conditions of social media cause legislators to behave differently, resulting in a comprehensive increase in legislator partisanship. This research holds importance in future studies by shedding light on how social media is used by those in our state governments, especially as it pertains to their party posturing online.

With more than a million arrivals in Europe in 2015, the mass migration has impacted the continent politically, economically, and socially. According to Europol, a law enforcement agency, in 2016 more than 90% of migrants going to the European Union have experienced some kind of smuggling during their journey. Many articles aggregate human smuggling with human trafficking. However, there is a lack of empirical examination and of different types of smuggling. In this study, I focus on migrant smuggling. Nevertheless, human smuggling is often seen as a crime that is organized, where a new “service” gets added to their principle activity (drug smuggling, money laundering…), as stated by Finckenauer, and performed by people who have never been involved in transnational criminal organizations. I assess this puzzle: How does human smuggling differ from human trafficking and organized crime, by examining primary and secondary sources such as scholarly literature and local newspapers? I argue that human smuggling has some traits of organized crime; however, considering entirely as it, limits the study of smuggling, as a business activity, and limits the prevention of illegal immigration. Human smuggling has traits of organized crime: the structure, the persistence, and violence have shown that these elements are not voluntarily present in the activity of smuggling. Nonetheless, the main purpose of this activity is treated as a business: where demand and supply meet and where price fluctuates depending on these factors. Migrant smuggling needs to be seen as a mutually consenting act between a migrant and a smuggler: the migrant is looking to flee because of instability in his/her country for the hope of a better life while the smuggler is considering smuggling as a business. This project aims to give a nuanced understanding of human smuggling to better intervene at the source of this mass migration.

After the establishment of a new democratic government following the US invasion in 2001, the Taliban has provided a parallel court system across Afghanistan. Rebel forms of justice may appear highly coercive, but prove particularly appealing to civilians if they are quicker, more straightforward, easier to access, or more in line with their preferences than formal state courts. Under what conditions are citizens more likely to support the legal authority of the formal government’s judicial systems compared to rebel groups’ alternative forms of law? In this research, I expand the framework of competitive governance by focusing on the decision of individuals to support various legal institutions based on views of government legitimacy founded in the evaluation of public service provision. I hypothesize that as individuals view the government as more legitimate, they demonstrate support for the formal courts, and as they view the government as illegitimate, they are less likely to support the formal judicial system. I address this question by analyzing two data sources. First, I have identified active Taliban courts by district from 2011-13. Second, I have access to the ANQAR survey data, an extensive nationwide survey addressing perceptions of government legitimacy and rule of law. Preliminary findings suggest a zero-sum dynamic between the institutions - that successful rebel governance may not only increase compliance with the rebels, but may actually decrease civilian compliance with state institutions. The policy implications of this research are twofold. First, this informs formal governments that building a judicial system is not just a function of strength and access, but rather that civilians are assessing the government’s service capacity as a whole. Secondly, understanding civilian behavior and preferences will assist nascent democracies in determining how it is most efficient to spend their limited resources to increase the support of the civilians.

Reinforcement Learning (RL) is a subcategory of machine learning, in which an agent (the decision maker) observes its environment and executes the best course of actions to maximize rewards. This is similar to teaching a pet to perform tricks using treats as positive reinforcement. Our research compares different RL methods on low-performance devices like a Raspberry Pi in real-time, real-world environments. RL has gained popularity recently with breakthroughs from DeepMind’s paper, Playing Atari with Deep Reinforcement Learning, where an agent learns to play Atari games from raw pixels and from DeepMind’s AlphaGo (DeepMind, https://deepmind.com/research/alphago) program that was the first computer program to beat a world champion Go player. RL projects like AlphaGo have utilized big data, powerful computing resources, and simulated environments that do not require real-time interaction to train the machine learning models. Our group compares the effectiveness of different RL methods on an accessible level of computing power on offline devices that an average consumer could acquire. The team constructs a physical environment for the robot to navigate, creates an OpenAI Gym environment that our agents will use to control the robot and get feedback from the environment. We train our agents using different RL methods to optimally navigate the environment and avoid collisions. We then compare the performance of the different methods in our physical real-time environment. Reinforcement Learning in small, offline devices could pave the way for a variety of devices that learn over time without being connected to a network. Imagine a small Mars rover that learns to navigate its environment efficiently over time.

This study evaluates whether more punitive state welfare policies depress rates of voter turnout in communities with high concentrations of welfare recipients. While scholars have studied the influence of demographic group belonging and the effects of state institutional contact (prison, welfare, etc.) on political participation and voting, researchers have not studied how voting behavior shifts in response to policy-level variations in welfare states. To address this gap in the literature, I use data from the Urban Institute's Welfare Rule’s database to generate a novel index of state welfare punitiveness that will be broadly useful for scholars interested in state welfare policies and political socialization. I use multivariate regression analysis to test whether high concentrations of welfare recipients in more punitive states systematically affects voting behavior. Results of this study will be relevant for scholars concerned with voting behavior, political learning, institutional contact, and democratic participation.

This study seeks to understand how to best support low-income, urban employment mobility through transportation. While scholars have studied the effects of urban planning, access to a private vehicle and public transportation for low-income residents, there is no consensus in the academic community for which method of transportation is most effective. Utilizing responses from the 2004 General Social Survey, the study measures perceived access to public transit, car ownership and employment mobility—the ability to switch to an equally desirable job— among low-income residents. Additionally, I employ census data measuring willing job-to-job transfers, car ownership and low-income public transit commuting time as a proxy for transportation access. I expect to find a positive relationship between car ownership and employment mobility, while I predict no relationship between public transportation and mobility. I utilize a multivariate regression analysis for the census data. This study has important implications for transit policy, as it can help inform how to better fund public transit to help low-income individuals and may lead to experimental policy like low-income car subsidies.

Since the 1990s, legislation in many US states has increasingly criminalized and restricted undocumented immigration. However, despite these restrictive policies, the population of undocumented immigrants has increased and overall immigration flows from Mexico to the US only began to decline following the 2009 recession. This research responds to decades of increasingly punitive policy and intends to test the efficacy of these policies. Many studies have focused on the efficacy and implications of federal policies, but far less attention has been given to state-level policies in the US. This paper analyzes the efficacy of punitive, state-level immigration laws from 2010 to 2016 in the United States. I hypothesize that there is no significant relationship between the passage of punitive state-level immigration policies and the year-to-year change in state populations of undocumented immigrants. However, I also hypothesize that the introduction of punitive state-level immigration policies affects yearly immigration flows—or the number of people immigrating from Mexico to a specific US State. To conduct this study, I created an index that aggregates a broad spectrum of laws, including policing, licensing, education, public benefits and labor. I then selected a few high-profile state laws intended to deter or curb undocumented immigration. I used multivariate regression analyses to test whether the introduction of punitive policy immigration policy systematically influences migration flows and changes in migrant populations while controlling for relevant economic and demographic factors.

Welfare policies in the United States perpetuate women’s oppression. This is largely because these policies reinforce sexism, racism, and classism that plague American society. When discussing policy options, scholars and political players should not just consider their economic consequences. They should emphasize the social consequences of these policies, such as how effectively they may combat women’s oppression. In this study I aim to demonstrate what an emphasis on vulnerable groups’ needs may look like and to further the political debate surrounding a federal jobs guarantee (FJG) and a universal basic income (UBI). I use the framework established in Justice and the Politics of Difference by Iris Marion Young and her explanation of the five faces of oppression to evaluate how FJG and UBI may help or harm women. I also analyze recent public opinion polling to determine the likelihood of the U.S. implementing these policies. This paper answers the following questions: Which policy, FJG or UBI, would more effectively undermine women’s oppression? Which is more likely to be implemented? Should those concerned with women’s oppression favor UBI, FJG, or a combination of the two? My theoretical analysis shows that UBI would more effectively combat women’s oppression. However, public opinion polling suggests that FJG is more likely to be implemented. This presents a dilemma for progressive advocates who wish to prioritize vulnerable groups’ needs while focusing on realistic goals. The social justice framework that I adopt in this paper resolves this dilemma — due to its inability to further the cause of gender equity, FJG should not be implemented unless it accompanies a form of UBI. These conclusions both contribute to ongoing debates over these policies and demonstrate how researchers and advocates going forward can analyze policies within a social justice framework that prioritizes the needs of our most vulnerable populations.