“The Politics of Abortion Care in Ohio” is based on 16-months (November 2021- February 2023) of ethnographic fieldwork and 47 semi-structured interviews conducted before and after the Dobbs Supreme Court decision (2022) overturning the right to abortion in the United States (US). Currently, 14 states have banned abortion and three have bans prior to six weeks of pregnancy. I assert that the criminalization of abortion care is a form of structural violence that leads to direct harm experienced by pregnant people. My dissertation strives to make significant contributions to theories of state-based violence with particular attention to reproductive governance, the anthropology of policy, and the politics of care. First, I develop my concept of reproductive gerrymandering, which names a particular phenomenon wherein the political power of voters who support reproductive healthcare access is suppressed across political party lines. It gives the false impression that the majority of residents in states that predominately elect Republican representatives want government elimination of abortion and related services. I argue that reproductive gerrymandering is a form of bureaucratic violence used to promote anti-abortion agendas, which then causes everyday structural harm to pregnant people. Second, building upon theories of agnotology, or the study of ignorance, I argue that “heartbeat” bans—legislation that advances medical misinformation—manipulates biomedical terms to imbue a particular social meaning to embryos at a very early stage of pregnancy. I explore how biomedical practices, in this case the use of ultrasound technology to detect a “heartbeat,” furthers the cultural production of ignorance around pregnancy and sends a strategic message about the beginnings of life. Third, I demonstrate how constant fluctuations in abortion policy shape temporalities of care in clinic settings. Finally, I reveal three overlooked dimensions of reproductive governance to better understand political control of reproductive bodies: administrative and regulatory, the spread of ignorance, and the political reconfiguring of reproductive time. Ultimately, I argue for the conceptual value of attending to temporalities of structural violence, and specifically the pace with which political violence unfolds.

This dissertation is motivated by a straightforward question about a drastic change to American politics: why has the process of staffing the circuit courts of appeals, once so agreeable and bipartisan, seemed to have descended into almost complete partisan bitterness? Across the entire time series, these are, after all, the same courts endowed with the same power of judicial review. And when the process of staffing them was harmonious, the courts were nevertheless deciding the fate of major, controversial policies of national importance---such as the New Deal in the 1930s and civil rights in the 1950s---just as they do today. Yes, many other aspects of American politics have changed through the decades. But what could possibly explain such a complete reversal of course? I argue that this change, toward divisiveness and partisan warfare, is actually about the judiciary itself and the substantive manner by which the nominees are thought of---namely, the entry of judicial ideology into the debate through the innovation of circuit judges being evaluated on ideological terms. While taken for granted as central today, any ideological assessment of circuit court nominees, and in particular viewing them as having a comprehensive judicial philosophy as opposed to just a position on singular pressing issue of the day, was almost nonexistent for generations. Its entry into the process was piecemeal and somewhat complicated, but it eventually came to dominate and irrevocably polarize the business of staffing the courts. I argue that this was the key factor that leaves us where we are today. Broadly speaking, I consider the contributions and particular strengths of my dissertation, relative to previous scholarship, to be threefold. First is my argument and accompanying analyses which put the crucial (and severely understudied) role of judicial ideology front and center. Second, I analyze the entire lifespan of the circuit courts, whereas the previous scholarship looks only at (often relatively brief) subsets of their history. As far as I know, this is the first study to systematically look at all circuit court nominations from the establishment of these courts in 1891 through the modern era. Third, I collect and analyze a great deal of new data. In particular I focus on systematically utilizing extensive archival resources and build two original data sets related to the Senate's public and private evaluation of judicial nominees; and while there is certainly a qualitative aspect to much of this research, I also synthesize and make sense of it with quantitative analysis. In chapter 1, I explain the puzzle motivating this research, elaborate my argument, and lay out the theoretical, methodological, and data collection contributions of this dissertation. I also review the literature and describe the three existing schools of thought. In chapter 2, I give an overview of the history of the circuit courts from their founding to the present. In this data-heavy chapter, I examine multiple metrics individually, and using several of these I build a robust composite score of divisiveness for each nominee ever made to the circuit courts, from 1891 through 2020. As far as I know this has never been done before. I find overwhelming evidence that the process has fundamentally changed and become more divisive. In chapter 3, I dig more deeply into the timing of this change, and begin to explore how and why it happened---and begin my attempt at demonstrating how the evaluation of judicial ideology is central to this change. To do this I examine a massive data source that has never been utilized: the Senate Judiciary Committee hearings for all nominees. With both qualitative and quantitative analysis, I show that the evaluation of nominees has varied widely over time. Prior to 1979, nominees were evaluated almost exclusively based on their qualifications, with ideology examined only under special circumstances, which I explore in depth. In this time period, ideological scrutiny predicted a contentious confirmation process, providing evidence for my argument that ideological evaluation drove divisiveness. Also in this chapter, I analyze the post-1979 transition to the routine ideological evaluation that permanently altered the confirmation process. I find that Republicans and comprehensive judicial philosophies both played a key role. In chapter 4, I examine the senators' private evaluation of nominees, in part to serve as a check on the validity of my earlier data analysis and also to see if there is any difference between the senators' public and private goals in relation to the judiciary. To do this, I build an original data set of over 1000 internal letters and memoranda from senators, by searching the archival records of nearly every president since Benjamin Harrison as well as over 150 senators. Studying this material qualitatively and quantitatively, the findings here largely align with the analysis of the public committee hearings: for much of history senators were concerned mainly about qualifications, with ideological concern rare and under special circumstances, but eventually ideology came to be the predominant concern which ended the consensual and placid process. This immense historical record also brings to light additional senatorial goals, such as ensuring residents of their own state as well as personal friends obtain judicial appointments. In chapter 5, I focus in on the post-1979 era and I find that the more ideologically distant a nominee is from the Senate, the more divisive the confirmation process is. This provides evidence that the process is defined by ideology related to the nominees, not garden variety polarization of the system. In chapter 6, I conclude, trying to synthesize all of my findings as well as offer some thoughts on areas of future research.

Despite decades of research on school effectiveness and improvement, we continue to struggle to support school improvement at scale. I suggest this is in part due to methodological and theoretical limitations of the extant literature: While there is a growing consensus that leaders should “diagnose” school improvement needs to devise contextually appropriate improvement strategies, no empirical guidance exists to support how to make such diagnoses or which strategies to employ given contextual variation. I address this gap through a mixed methods analysis of how schools with varying improvement capacity at the onset of the COVID-19 pandemic in New York City adapted and learned in response to the pandemic’s challenges. While the COVID-19 pandemic has presented school and district leaders with a more extreme context in which to confront a central problem that has confounded educational researchers and practitioners for decades: How can school and district leaders build the improvement capacity that will enable them to continuously meet the needs of all students in all schools? There are five key findings of the present study. First, using Latent Transition Analysis (LTA), I identified six statistically significant subgroups among all New York City elementary, middle, and high schools serving students in grades 3-8 (n=1225) based on teachers’ perceptions of school improvement capacity. I further described the relationship between this typology and school contextual covariates and student outcomes, depicting the types of schools that are classified in each subgroup and the relationship between subgroup classification and academic outcomes. Second, I demonstrated how teachers’ perceptions of school capacity varied from 2017-2019 and further identified a differential relationship between principal turnover and school improvement trajectories. Third, I found strong qualitative support for the quantitative typology, describing alignment between teachers’ and leaders’ lived experiences of school improvement and change and the quantitative typology and trajectories with one key exception: those schools that experienced a leadership transition after 2019 were most likely to have experienced dramatic change. Fourth, I found teachers’ and leaders’ perceptions of challenges associated with the COVID-19 pandemic varied, in part, as a function of their improvement capacity at the onset of the pandemic. Respondents in schools from high-capacity subgroups were more likely to view pandemic challenges as easy to overcome, while respondents in schools with minimal improvement capacity were more likely to be overwhelmed by the multiple, compounding challenges they faced during the pandemic. Finally, I found key differences in the strategies schools employed to adapt and learn in response to these challenges, which again, varied based on their improvement capacity when the pandemic hit. Together, these findings provide support for a theory of differentiated school improvement.

This study examines the various ways that Central American migrants traversing Mexico’s southern border interpret, negotiate, and resist conditions of immobilization imposed by state refugee policy and other institutional impediments to northbound movement. My findings are informed by 12 months of ethnographic fieldwork in Tapachula, Chiapas, followed by an additional six, non-consecutive weeks in various sites of transit across Mexico as a Human Rights Observer in the migrant caravans of 2017 and 2018. Since 2011, as a result of increasing rates of violence, flows of Central American women, youth, and families across Mexico’s southern border have risen substantially. In efforts to curb northbound movement, the US has exerted significant pressure for the Mexican government to assume a greater role in the retention, organization, and deterrence of prospective refugee populations, resulting in the temporary resettlement along the southern border of thousands of migrants seeking international protection. Many of these migrants find themselves in a liminal space of legal and social uncertainty in which they must contend with a range of limitations and distinct possibilities as they consider their ongoing trajectories. Through close attention to the social worlds that emerge around and within migrants’ transit communities, I explore central themes related to the existentiality of im/mobility, gendered experiences of transit migration, the paradoxes of institutional practices of refugee protection within predominant transit zones, and diverse forms of resilience and coping that are given breadth through collective travel. Ultimately, I argue that it is critical to explore the narratives and lived realities of those most affected by migration-centered policy and discourse, and to recognize the critical role that migrants play in challenging and reimagining the terms of their in/exclusion.

Numerous studies have examined the effect of natural disasters and social vulnerability indicators on broad factors using different theoretical frameworks; however, no study synthesizes a framework of theories to comprehensively examine the nexus among natural disasters, social vulnerability, subsidiarity principle, and government finance in the U.S. The dissertation utilizes a broad framework of theories to address two objectives of the dissertation namely the determinants of (1) infrastructure investment and (2) public assistance programs. Using panel data from 50 U.S states over a 17-time period (2000 – 2017), the first objective of the study investigates the determinants of infrastructure investment using a synthesis of theories. The study maintains that state infrastructure investment is not necessarily because of disaster punctuations but economic prosperity. The findings paint a picture of a proactive government that is perhaps prepared and does not act necessarily because of disaster events. The objective of the second study investigates the determinants of public assistance receipt assessed in 4 ways. The study maintains that subsidiarity assistance is influenced by both disasters and social vulnerabilities. However, disaggregating subsidiarity assistance into its sub-components (total public assistance, emergency work funds, permanent work funds, and intergovernmental transfers) provide a nuanced understanding of public assistance receipt. Federally declared disasters are strongly associated with disaster-relief forms of assistance, but not a significant determinant of intergovernmental transfers. Social vulnerability indicators, more especially – poverty, inequality (Gini), political influence, income, and infrastructure investment positively influence the receipt of federal intergovernmental transfers. The findings demonstrate that federal allocation of subsidiarity assistance follows a formula in terms of addressing wicked problems related to disasters and social vulnerabilities. In addition, the dissertation finds that disaster impact and social vulnerability theory additively explains the subsidiarity principle of U.S. federal government assistance programs to subnational governments. The dissertation contributes to theory, policy and management practices, and enlightens scholars and policymakers on the vital factors that stimulate state infrastructure investment and federal assistance receipt for best policy practice. Policymakers must constantly modify funding policies and effectively utilize tax-payer dollars following a formula to respond to jurisdictional vulnerabilities and public service needs

Divorce and the Politics of the American Social Welfare Regime, 1969-2001 asks how rising divorce rates shaped the laws governing the American social welfare regime between 1969, when California passed the nation’s first no-fault divorce law, and 2001. Scholars have shown that in the early 20th century the American social welfare regime developed to distribute economic resources, such as Social Security, to women through their husbands. Between 1967 and 1979, however, the United States’ divorce rate doubled. This dissertation investigates how this sudden challenge to the breadwinner-homemaker family structure affected the gendered welfare regime. Divorce and the Politics of the American Social Welfare Regime examines how women organized to gain access to lost economic resources after divorce and how policymakers responded to their demands. It reveals important and forgotten components of the histories of welfare state development, the feminist movement of the 1970s, and marriage law. It argues that, ironically, rising divorce rates led to a series of federal laws that actually strengthened the social welfare system’s use of marriage to determine eligiblity for benefits. These new laws specifically rewarded intact marriages by providing more robust benefits to women in longer marriages. In a political world increasingly concerned with the impermenance of marriage, Congress created a legal system that signaled that marriage was about length of commitment above all else.

This dissertation includes three papers examining role of childcare stability in the lives of families, focusing on low-income households. Paper one considers the relationship between household instability and childcare instability for parents with young children in New York City. Paper two examines the relationship between universal pre-kindergarten in New York City and education and employment outcomes for parents. Finally, paper three explores the effect of federal childcare subsidies on education and employment outcomes for parents.

Over the past decade, and accelerated due to the COVID-19 pandemic, there has been increasing adoption of decentralized diagnostic testing, where the testing is brought closer to the patient. This trend has largely been fueled by the development of more accurate diagnostic tools and faster and more reliable data connectivity. Decentralized testing has been shown to greatly reduce turnaround times while increasing accessibility to users in remote regions. However, there are challenges that limit its widespread adoption. In this dissertation, we detail the development of tools and technologies to overcome these barriers and expedite the shift towards decentralized diagnostic testing. First, we demonstrate the ability to develop point-of-care (POC) diagnostic tests with performance that rivals that of traditional lab-based methods. We developed a rapid, multiplexed, microfluidic serological test for Lyme disease, a tick-borne disease caused by the Borrelia burgdorferi bacterium. The recommended testing, the standard 2-tiered (STT) approach, is not sensitive for early-stage infections, is labor-intensive, has long turnaround times, and requires the use of two immunoassays (enzyme-linked immunosorbent assay (ELISA) and the Western Blot). We developed a standalone multiplexed sandwich ELISA assay and adapted it to the mChip microfluidic platform. We validated the assay on a rigorously characterized panel of human serum samples and demonstrated that our approach outperforms the STT algorithm on sensitivity while matching its specificity. The form factor of this technology is amenable to use in physician’s offices and urgent care clinics. We also showed exploratory work towards adapting the mChip platform for diagnosis of Zika disease, a mosquito-borne disease caused by the Zika virus, and acute kidney injury, a syndrome characterized by loss of kidney excretory function. Next, we worked on increasing the adoption of rapid diagnostic tests for self- and partner-testing designed to be used in at-home settings. We developed a smartphone application to be used alongside the INSTI Multiplex test for detecting HIV and syphilis infections. The application was designed to provide users with i) instructions on running the test, ii) an automated deep-learning-based image interpretation algorithm to interpret the rapid test results from a smartphone image, iii) a way to save test results and display/share them, and iv) resources for follow-up care. We adopted a user-centered, iterative design process where we worked with a cohort of study participants composed of men who have sex with men and transgender women at high risk for contracting sexually transmitted infections. We then field tested the application with 48 participants over a duration of three months and found high acceptability for the application, both in terms of functionality and helpfulness. Finally, we sought to address a key limitation with deep-learning-based image classification techniques, specifically, the requirement for large numbers of annotated images for training. We developed a deep-learning image interpretation algorithm that could be quickly adapted to new rapid test kits using only a fraction of the images that would otherwise be needed for training the model. The interpretation algorithm followed a three-step, modular process. First, the rapid test kit and the membrane were extracted from the smartphone image. Second, the constituent zones were cropped from the extracted membrane. Finally, a classifier detected the presence or absence of a line in the individual zones. Fast adaptation was demonstrated by adapting a base model, trained using images of a single COVID-19 rapid test kit, to four different rapid test kits, each with different form factors, using few-shot domain adaptation. After training with 20 or fewer images, the classification accuracies of all the adapted models were > 95%. This approach can provide a digital health platform for improved pandemic preparedness and enable quality assurance and linkage to care for consumers operating new LFAs in widespread decentralized settings. Together, these methods provide a suite of tools that could expedite the shift towards decentralized, POC testing.

Master of Arts / This thesis is an exploratory study that aims to identify whether it is adequate to apply the current approach of considering policies related to geothermal energy under the general umbrella of renewable policies or we need to use a system-sector based approach specifically for geothermal energy systems. I have identified a total of twenty-three different policy types related to geothermal energy systems in U.S. states. To understand how geothermal related policies diffuse from one U.S. state to another, and, therefore, better design policies to promote the use of geothermal energy in U.S. states, we need to perform several diffusion studies. This process is time consuming and expensive. Thus, focusing on the most promising geothermal related policies, at least as a start, is crucial for future studies focusing on the diffusion of geothermal related policies between U.S. states. Therefore, this thesis focuses on the preliminary step of selecting a limited set of geothermal related policies for future policy diffusion studies. The main conclusions and answers provided in this thesis provide a strong support to the hypothesis that a system-sector based approach is needed when studying policies related to geothermal energy in U.S. states. I explicitly report that each of the three main geothermal systems is impacted by different set of policy categories and types. I also discuss that not all policies have the same impact on all sectors in which the geothermal energy is applied; in other words, the utilization of geothermal energy in the different sectors is promoted by different policies in distinguished ways. Moreover, the discussion in this thesis highlights the shortcomings of the common approach usually used in diffusion v studies of renewable energy policies. This approach considers all renewable energies as a general category, neglecting any potential impacts due to the unique characteristics of each renewable source. I show that, for example, the most popular policy types considered in policy diffusion studies for renewable energies are not the most significant ones for the different geothermal systems. I also highlight the fact that other policy types that are generally overlooked in policy diffusion studies of the generalized renewable energies are more significant for geothermal energy systems. These results indeed support my hypothesis regarding the importance of system-sector based approach when investigating geothermal energy policies.

In the first chapter of this dissertation, I exploit the Covid-19 pandemic and associated government restrictions as a natural experiment in order to study the resilience of businesses in the United States. I use a border-county identification strategy with data on government restrictions, employment and open small businesses, in order to assess the resilience of small businesses in the United States. In my main results, I find negative impacts of stay-at-home orders on the number of open small merchants. In particular, shutdowns of businesses accelerated 8 weeks after imposition of a stay-at-home order, suggesting that many businesses were only resilient enough to handle adverse conditions for 8 weeks. On average, a county with a stay-at-home order experienced an additional 1.51 percentage points loss in the number of open small businesses, relative to January 2020, 8 weeks later compared to a neighboring county that did not have a stay-at-home order. Firms were quicker to resort to layoffs. On average a county with an active stay at home order in a month experienced an additional 1.19 percentage point loss in employment, relative to January 2020, the following month compared to a neighbor that did not have a stay-at-home order the previous month. My results suggest that in future scenarios where governments consider enacting similar restrictions further aid is needed for businesses in order to help them stay afloat. In particular, more assistance should be delivered to businesses within two months from the enacting of the order. In the second chapter of this dissertation, I study economic spillovers in the context of theCovid-19 associated government restrictions. I use a detailed geolocation dataset to construct data on the number of visitors per-capita between neighboring counties in the early stages of the pandemic, which I use as a proxy for economic spillovers. I employ a similar border-county identification strategy as in the first chapter to identify the causal effect of stay-at-home orders on inter-county movement. Additionally, I provide evidence for an assumption used in chapter one by examining if there are reduced spillovers in county-pairs that lie in the different commute zones. I find that stay-at-home orders caused reductions in inter-county visits in both directions in a county-pair. That is, I find a decrease in travel from the county without a stay-at-home order to the county with one, as well as a decrease in the opposite direction. On average, a county that does not have stay-at-home order will receive 408 fewer weekly visitors from their neighboring county that has a stay-at-home order. I also examine the effect of stay-at-home orders on the ratio of travel between the two directions in order to find evidence of a net spillover effect between the two counties and fail to find evidence of a net spillover effect. I also find that spillover effects are indeed reduced in neighbor county-pairs where the two counties are in different commute zones. The results of this paper imply that residents in counties with stay-at-home orders decreased travel to their neighboring counties even when those counties did not issue their own orders. In future situations where policy makers need to consider similar restrictions, they should focus on acting more quickly and not be concerned if neighboring counties are not cooperative. In the third chapter of this dissertation, I test the predictions of career concerns models by studying Major League Baseball umpires. Major League Baseball games can be dramatically shaped by minor lapses in judgement from the umpires officiating the game. Due to the indefinite length a game may have, this can include having the game shaped in a way that ends it faster. I study whether evidence for the career concerns model can be found in baseball umpires. A career concerns model would suggest that older umpires, whose careers and reputations are much more established than younger ones, would be more inclined to improperly make judgements that favor the end of the game in extra innings. I use data on MLB umpires and extra-innings games from the 2010-2018 seasons to conduct my empirical analysis and use a linear probability model to isolate the impact of the umpires’ tenure on the probability they make a “bad call.” I find evidence supporting the career concerns hypothesis and that the probability that an umpire makes a bad call that shortens the length of the game and allows them to go home increases with their tenure. I show that these results are likely driven by career concerns, rather than carelessness, by showing their error rate does not increase with tenure in situations where it would not reduce their workload.