I study the impact that the Denver metropolitan area’s Regional Transportation District (RTD) commuter rail, opened from 2016 to 2020, had on commuting patterns in the region. Specifically, I estimate a dynamic difference-in-differences model centered around the openings of the A, G, and N rail lines, using areas of the region with older light rail transit lines as a control group. Using American Community 5-year panel data from 2011 to 2023 for commuting times and commuting mode shares by census tract and year, I found null effects on these outcomes for the G and N lines and multiple notable results for the A line, which is considered the RTD’s “flagship line.” My work finds that the A line saw mixed success in some of the metrics I evaluated. The A Line was associated with a temporary relief in car congestion in the line’s vicinity; I estimate that it was responsible for an increase in the share of car commuters commuting less than 30 minutes to work of up to 6.8 percentage points following the line’s opening, an effect which later subsided. I also estimate that the opening of the line was actually responsible for a decrease in the public transit mode share among commuters, leading to a decrease in bus commuter mode share of 2.5 percentage points yet an increase in train commuter mode share of 1.4 percentage points, an impact which I attribute to cuts in bus service following the new rail line. This result appears to show potential shortcomings of rail in replacing bus service. In conclusion, I argue that ridership can be increased through transit-oriented development (TOD) along commuter rail lines.

This paper intends to provide a qualitative description of an experimental alternative education program for formerly incarcerated adolescents. In doing so, it intends to construe this school as a site of liberation - in opposition to its former iteration as a correctional institution. It examines the school’s architecture, pedagogical practices, and practices around care to reveal that this institution has been reconfigured to empower students in any number of ways - from meeting their material needs to providing the conditions for immense intellectual enrichment. This ethnographic exploration may be used as a manual for replication or as fodder for thought on what education can do and mean.

During the last military dictatorship in Argentina, also called the Proceso de Reorganización Nacional (National Reorganization Process, 1976-1983), General Jorge Rafael Videla and the other members of the leading juntas sought to subdue the extremist violence that had built up over several decades. Yet, the junta used its mandate to persecute “subversive elements” that it saw as threats to its ideal of a Western and Christian society: political opposition, intellectuals, students, religious minorities, and even apolitical citizens. In this brutal repression operation, also referred to as the Guerra Sucia – the Dirty War – the military government abducted, “disappeared,” tortured, and killed an estimated 30,000 people. After a defeat in the Falklands/Malvinas War, the junta was replaced by Raúl Alfonsín’s elected civilian government in 1983, which implemented a series of policies designed to bring truth, justice, and accountability – core to a concept scholars call “transitional justice” – to Argentina. This effort, which included the creation of a truth commission, the prosecution of perpetrators of repression, and the reparation of victims, was essential for the construction of a new, legitimate, and robust democracy. Through transitional justice, the government sought to repair the social ruptures caused by the Dirty War and ensure that the human rights violations would not happen again. While Alfonsin and his successor Carlos Menem facilitated a period of impunity between the late 1980s and the early 1990s, the pursuit of truth, justice, and accountability continues to this day. While the role of domestic dynamics in this process has been studied extensively, this thesis seeks to uncover the role of other valuable players: transnational non-state actors. These organizations, which transcend boundaries to enact change, are crucial in a world governed by international norms. Thus, my thesis investigates the question: How did transnational non-state actors help promote truth, justice, and accountability throughout the Argentine democratization process? By focusing on three types of transnational non-state actors, regional human rights bodies, international non-governmental organizations (INGOs), and philanthropic foundations, this research highlights the mechanisms used by these organizations to promote transitional justice and democracy from abroad – both during and after the National Reorganization Process. To achieve this, I explore archival material and secondary sources, supplemented by a variety of expert interviews. Ultimately, I argue that by elevating the Argentine human rights situation into the global public sphere, supporting domestic activity, and pressuring both the Argentine and foreign governments to uphold international norms, transnational non-state actors effectively fostered truth, justice, accountability, and democracy.
This research is particularly important in the current era of democratic backsliding, in which elected leaders are consolidating power, eliminating protections for rights, and questioning international norms. I argue that by implementing policies to weaken transnational non-state actors, leaders are tacitly eliminating a fundamental democratic safeguard. By strengthening transnational non-state actors from within, it will be possible to counteract this trend and preserve democracy.

Previous research and policy debates regarding the topic of static images generated by artificial intelligence platforms have explored the effects of photorealistic AI-generated media on public perceptions of the truth and the potential of these images to escalate online disinformation, particularly during elections in the United States. While this research is crucial to understanding the consequences of convincing AI-generated images and media online, the current quality of AI-generated images that everyday social media users create or interact with is often far from realistic. These low-quality images lack the critical aesthetic details often found in higher-quality AI-generated images. Yet, low-quality AI-generated images frequently receive high levels of engagement through likes, comments, or shares on social media platforms, such as Meta’s Facebook and Instagram or the platform X (formerly known as Twitter). Considering the contentious nature of creating low-quality AI-generated images in spite of increasing integration of generative artificial intelligence into online platforms, this type of media exists as a complex yet under-researched form of digital creation. This study collects survey data to address this gap in current research on artificial intelligence, emphasizing the role of low-quality AI-generated images. The objective is to assess how users perceive the motivations behind the creation of these images as well as their engagement patterns with these images based on the following attributes: believability, accuracy, informativeness, humor, and deception. Findings generally support increased negative perceptions towards low-quality, AI-generated images while high- quality, AI-generated images are generally found to be perceived as more believable, accurate, and informative. Advanced age and partisan affiliation also have significant effects on perceptions and engagement intentions with AI-generated media. Informed by these results, social media platforms should support the use of more labels across all AI-generated media. Furthermore, policymakers should pursue legislation that can enforce this method of moderation, protecting the rights of users, platforms, and creators in the process.

Students from wealthy backgrounds have long dominated elite universities in the United States. Still, in recent decades, institutions like Princeton have expanded financial aid to make college more accessible for low-income and first-generation college students. While these initiatives increase socioeconomic diversity, disparities in students' experiences persist, particularly in terms of social fit, access to resources, and institutional support. Existing research highlights the challenges low-income students face at elite institutions. Still, little attention has been given to the role of Adverse Childhood Experiences (ACEs) in shaping these students’ pathways to and through college. This study examines how Adverse Childhood Experiences (ACEs) influence the experiences of low-income students at Princeton. Drawing on survey data from 55 undergraduates and semi-structured interviews with 25 of these students, this research explores their backgrounds, motivations, and challenges in navigating university life. These findings reveal that while Princeton provides financial support, it does not capture students’ struggles with resource navigation, social belonging, and emotional well-being, highlighting gaps in our understanding of adverse childhood experiences in the context of resilience. This study highlights two areas for growth: universities must extend their efforts beyond providing financial access to students to achieve true inclusivity. While acknowledging that early-life adversity continues to have lasting implications in adulthood, the ACE scale does not account for other childhood adversity that underlies their experiences.

This thesis examines the ongoing influence of parental divorce during early years on how parent-child relationships develop as these children grow up and attend college. Most of the studies spotlight the immediate consequences of separation. However, this research delves deeper into how these dynamics shift after custody arrangements end and students step into autonomy. Drawing on twelve in-depth interviews with college students from divorced families, this study highlights four main themes: freedom from household alternation, uneven parental involvement, emotional stress during school breaks, and efforts to rebuild or redefine relationships in adulthood. Through qualitative analysis, the research shows how students reflect on their family past, set boundaries, and redefine love and support on their terms. This study explores how young adults manage parent–child relationships after divorce, highlighting resilience, growing independence, and shifts in self-awareness. It is beneficial to understand the long-term impact of divorce on identity and family connection.

Green extractivism, a term attributed to Uruguayan social ecologist Eduardo Gudynas, refers to a process that reproduces and reifies existing material inequalities through the exploitation, dispossession and neocolonialism by development projects categorized as “green” or “sustainable.” The framework refers to a wide encompassing array of industries including hydroelectric power, critical materials, agriculture, and infrastructure. This framework is consolidated by Alexander Dunlap to describe an evolved form of capital accumulation designed to accommodate new political ideologies and priorities associated with global anthropogenic climate change. Existing legal frameworks in Latin America and the Global South, namely the International Labor Organization Convention no. 169, and the United Nations Declaration on the Rights of Indigenous Peoples (UNDRIP), normatively empower Indigenous Peoples (IP) to exert their land and resource rights in the face of green extractivist projects through the process of prior consultation, or consulta previa, and Free, Prior, and Informed Consent (FPIC). This paper employs a mixed-method approach to a case study of Guatemalan consultation procedures and their relationship to the emerging green extractive model. Using quantitative data in the form of a compiled dataset of hydroelectric projects on Indigenous territory in Guatemala, and a comparative analysis of mining and hydroelectric projects in Guatemala, I find that hydroelectric mega-projects are subject to less intense scrutiny in the consultation process than other extractive industries in Guatemala, specifically mining, firmly placing them within the extractive model. Furthermore, I recommend that the principles of Free, Prior, and Informed Consent should be regarded as the dominant framework, beyond “soft law,” to inform consultation proceedings with Indigenous Peoples ahead of extractive projects.

This thesis critically examines Singapore’s human capital development model, which has been internationally lauded for its education system, workforce policies, and emphasis on meritocracy. Built on the premise that success should be determined by talent and effort rather than background, Singapore’s meritocratic framework has fueled its economic ascent and cultivated a globally competitive labor force. However, beneath this narrative lies a paradox: while intended to promote equity and upward mobility, the meritocratic ideals underpinning Singapore’s policies often reinforce existing inequalities. Through a qualitative policy and discourse analysis, this study investigates the contradictions within Singapore’s approach to education and workforce development—focusing on key policies such as the Primary School Leaving Examination (PSLE), bilingual education, and the SkillsFuture initiative. It interrogates the state’s evolving rhetoric around fairness and opportunity, revealing how structural barriers related to class, race, and access to resources continue to stratify success. The thesis argues that although the Singaporean government has introduced reforms aimed at mitigating these disparities, these efforts tend to preserve, rather than fundamentally transform, the meritocratic system. By unpacking how policy intent, implementation, and public discourse intersect, this study contributes to broader debates on the limits of meritocracy, the complexities of equity in state-led development, and the global relevance of Singapore’s model. Ultimately, it calls for a more inclusive and reflexive approach to human capital policy—one that balances the pursuit of efficiency with a deeper commitment to social justice.

This study aims to develop software for modelling electrodes covered in a porous insulating layer for applications to carbon dioxide reduction. The project focuses on modular design and efficiency in its construction of these modules. It successfully produces a library for cyclic voltammogram analysis including visualization and baseline correction. It also develops a data structure for partitioning 3D space, allowing efficient collision checking. Finally, it presents designs for electrode modules that allow clients to specify the geometry and conductivity of the electrode surface.

This study examines how pharmaceutical companies strategically adjust their lobbying activities in response to regulatory threats, explicitly focusing on the Inflation Reduction Act (IRA) of 2022. Using a difference-in-differences methodology comparing firms directly affected by Medicare price controls against less-affected peers, the research demonstrates that companies with products subject to IRA price negotiations significantly increased their lobbying expenditures by approximately $636,460 per quarter following the legislation's passage. Analysis of quarterly lobbying data from 2018-2025 for the top 20 U.S. pharmaceutical firms reveals that affected companies concentrated their lobbying efforts after the IRA's enactment, emphasizing ex-post influence over implementation details rather than prevention. A detailed case study comparing Pfizer and Sanofi illustrates how lobbying intensity directly correlates with regulatory exposure: Pfizer, with multiple products targeted for price negotiations, dramatically escalated its political engagement across multiple channels, while Sanofi, facing minimal immediate impact, maintained relatively stable lobbying activities. These findings contribute to understanding corporate political strategy by demonstrating that regulatory exposure drives lobbying intensity in a proportional, targeted manner. The research suggests that pharmaceutical pricing reform triggers sophisticated multi-channel political mobilization to shape implementation to mitigate financial impacts. This explains historical difficulties in achieving meaningful drug pricing reform and has implications for designing more effective healthcare policy that minimizes vulnerability to industry influence.

This thesis examines the progression of portfolio optimization techniques from traditional (Markowitz and CAPM) to much more computationally advanced techniques such as Machine Learning and LLMs. Using a 15 year dataset of daily S&P500 returns, we show that Long Short-Term Memory (LSTMs) excel at capturing much shorter-term return forecasting compared to Deep Neural Networks (DNNs) which excel at discerning complex, otherwise invisible patterns in the long term and map straight from input data to asset weights. Both approaches surpass classical benchmarks in risk-adjusted performance. Lastly, we introduce a Large Language Model (LLM)–based simulator, demonstrating how ChatGPT can effectively synthesize (e.g., news headline sentiment, policy announcements) into allocation decisions. Our findings highlight the promising future of prompt engineering as well as LLM’s promising ability to combine numerical and textual insight into, potentially, better understood portfolio strategies.

Adolescence is a sensitive period of life shaped by environmental factors, including socioeconomic status (SES) and early adversity. While previous studies have linked early adversity to cognitive and physical outcomes, it remains unclear how these effects vary by income level or whether physical maturation mediates the influence of early adversity on cognition. This thesis investigates how early adversity—specifically prenatal stress and early life stress (ELS)—impacts adolescent cognitive development across income groups and explores whether physical development mediates these relationships. Using longitudinal data from the Adolescent Brain Cognitive Development (ABCD) Study, I compare cognitive and physical development between high- and low-income adolescents and apply linear mixed-effects models and mediation analyses to examine potential pathways. First, I show that high-income adolescents undergo earlier cognitive development, while low-income adolescents experience earlier physical development. Then, I demonstrate that both prenatal stress and ELS are significantly associated with cognitive and physical development in the high-income group, whereas only prenatal stress is significantly associated in the low-income group. This indicates that among low-income adolescents, the influence of ELS may be overshadowed by broader adversities prevalent in low-income environments. Expanding on this finding, I reveal that prenatal stress exerts a stronger influence than ELS on slowing cognitive development and accelerating physical development in the high-income group. Finally, I show that the timing of physical maturation does not mediate the relationship between early adversity and cognitive outcomes, suggesting that the stress acceleration hypothesis cannot be extended to cognitive development. I conclude by proposing that alternative mechanisms—such as emotional development—may better explain how prenatal stress and ELS shape cognitive trajectories, implying the need for interventions aimed at mitigating the long-term effects of early adversity on adolescent cognitive development.

Yellow Fever (YF) caused by yellow fever virus (YFV) has historically been a threat to global health. This risk posed by YFV was highly mitigated with the development of the vaccine strain YFV-17D, but challenges caused by this virus linger despite global vaccination attempts. With outbreaks occurring in urban areas in the past 20 years and the lack of an antiviral for YFV, there is demand for expanding the arsenal of global health tools to fight against Flaviviruses like YFV.

To address this gap, a high-throughput screen was done on the ~75,000 molecules in the Princeton University Small Molecule Library to find a compound that exhibited antiviral activity against YFV while also maintaining low cytotoxicity. The initial screen identified 626 molecules with antiviral activity, of which 43 were determined to be noncytotoxic. While these initial stages of the screen seemed promising, we were unable to identify a molecule that consistently exhibited antiviral activity. Of the 43 molecules that continued to the second round of screening, 5 exhibited a titratable effect where an increase in concentration led to a decrease in viral activity. Those 5 were preliminarily retested but did not validate the original antiviral activity exhibited in the initial screening process.

Due to time constraints, more thorough workup could not be completed to establish firmly whether any of the compounds are suitable for further refinement in structure activity relationship analysis. It is also conceivable that some of these compounds may exhibit antiviral activity against other related flaviviruses. Moreover, the search for an antiviral that acts on YFV can continue by screening other molecule libraries or identifying targets in the YFV replication cycle.

Social support is an essential component of emotional and physical well-being and has been implicated in cardiovascular disease risk as well as the experience of daily stress. This study primarily seeks to understand potential differences in receiving versus giving social support and their relationships with cardiovascular disease (CVD), along with an auxiliary focus on the relationship between daily stress and CVD. Through a secondary analysis of the Midlife in the United States study (Wave II), we examined the relationship between daily stress and CVD, the relationship between receiving versus giving social support and CVD, and how these two forms of social support interact in their links to CVD. We found that daily stress does not robustly predict cardiovascular disease, but both receiving and giving social support are associated with a reduced cardiovascular disease risk. However, giving social support ceases to confer protective cardiovascular health benefits on the giver when they receive high levels of social support. Moreover, receiving social support was consistently associated with a lower CVD risk, but the effect was greater when participants were giving a low level of support. These results illustrate that the effects of receiving and giving social support are not necessarily equivalent and should be considered distinctly and simultaneously, rather than in aggregate, in efforts to mitigate cardiovascular disease risk.

cemented. Yet, the boundaries of what's permissible and impermissible may also fluctuate and develop as a result of these catalysts of change. While there is nothing inherently wrong with progress, unchecked evolution may become domineering and furthermore oppressive. The purpose of this thesis is to evaluate Princeton's progress and, furthermore, the institution's current actions. Essentially, it is to evaluate the fairness, wisdom, and overall accurate judgment of their response against the Palestinian protesters. However, this evaluation will not be done by a student, post-doc researcher, or member of the faculty. Rather, the actions of the past will be called to judge the performance of the present. Using Princeton's past interactions with student-led protests, this paper will examine similarities, consistencies, and, importantly, discrepancies between various eras of protests and the most recent War on Gaza protests. As such, this endeavor will be done through the analysis of four specific eras of Princeotnian protests: The Vietnam era, The South African Apartheid era, The Black Lives Matter era, and finally, the War on Gaza era. In each of the eras, there will be three main connecting variables of comparison. Firstly, the demands of the activists. What was the central issue or problem that the group was protesting against? How do the demands of the group illustrate their values and, furthermore, their relationship with the public and Princeton? The next variable of analysis is the form of expression, i.e., Protests, sit-ins, etc. How did the specific student group express their ideas? Were they confrontational and disruptive? Lastly, the final mode of analysis is the university's response. In other words, how did the university interact with the actions of the student group? Did they meet them with hostility or restraint? Were they deaf or receptive to the demands of the student group? Nevertheless, why are these the three main variables of analysis? These variables are important in understanding Princeton's management of the War on Gaza protests. For instance, if throughout the four cases we find a stark similarity of demands and forms of protests; however, the Institutional responses on the War on Gaza was inherently harsher than other cases , this would provide evidence of inconsistencies of management, hypocrisy, and most importantly discrimination. In other words, let us presume the protestors in the BLM or Apartheid era performed eerily similar protests as their War on Gaza counterparts did and, furthermore, had inherently similar demands. Yet, the Princeton administration treated the War on Gaza protesters in a more brash and harsher way. While this is not on-the-dot evidence for straightforward crimes, it would still pose a problematic situation for Princeton. If an analysis such as this were to happen, a deeper understanding of why Princeton veered away from its methodology of the past would need to be performed. Questions revolving around the notion of what made Palestinian protesters different from Vietnam, Apartheid, and Black Lives Matter protesters if their demands and forms of protesting were similar. Nonetheless, in order to get to that train of thought, the following analysis has to be made. In the next four chapters, each respective era will be analyzed based on this methodology.

Artificial neural networks (ANNs), particularly large language models like GPT-2 and BERT, have demonstrated remarkable capabilities in language comprehension and generation, prompting questions about the extent to which their computational processes resemble those of biological neural networks (BNNs)—the dynamic, adaptive systems of the human brain. This thesis investigates whether ANNs and BNNs share fundamental computational strategies in language tasks, specifically differentiating between task-general computations (predictive coding, context-sensitive representations, hierarchical processing) and task-specific ones (task flexibility and latent cause inference). Using methodologies such as encoding models, representational similarity analysis (RSA), attention-head mapping, and zero-shot generalization, the thesis identifies areas of alignment primarily under conditions of restricted modalities. Notably, predictive coding shows strong surface-level similarity between systems; however, deeper exploration reveals significant divergences in context sensitivity, hierarchical processing, and especially task flexibility—where ANNs fail to replicate human-like interpretation. These divergences underscore crucial limitations in current ANN architectures, highlighting their lack of cognitive scaffolding necessary for genuinely flexible comprehension. This thesis concludes by suggesting future research directions, including integrating multimodal inputs and embedding dynamic memory structures into ANN training, to better assess and narrow the cognitive gap between artificial and biological systems.