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The Politics of Emotions in the Age of Trumpism: Right and Left Variants
No full textThis paper analyzes the political importance of emotions in the age of Trumpism – particularly loss and sadness, shame, fear, distrust, anger, and hope - and how political actors both on the right and the left have tried to activate and channel those emotions. Political life is often portrayed as the realm of reason and interests. However, any careful analysis of politics shows that the mobilization of emotions is an intrinsic part of politics and a force for good as much as ill. Right wing actors – most notably Donald Trump -- have been quick to recognize and mobilize emotions. There is every reason to think that his Republican successors will also be speaking strongly to people’s emotions. For many on the left, political appeals relying on emotions are deeply disquieting. Appeals to emotion are associated with the vicious rhetoric of European fascist leaders in the 1930s and 1940s and their American analogues on the far right then and now. Yet emotions can be very important to left policy and politics. The great progressive surges of the New Deal and the Great Society could not have happened without the role of emotions such as fear, anger, and hope in driving the rise of the labor movement of the 1930s and the civil rights and successor movements of the 1950s through 1970s. For the left, there can be no escape from the need to understand and harness emotions in politics. An appeal to the pervasive sense of loss, shame, fear, and anger in our society may be crucial to winning back a substantial portion of the working class voters – both white and nonwhite – captured by Trump. Moreover, if powerful emotions are spoken to only by the right, they are likely to be mobilized and taken in directions quite inimical to the values of the progressive left.
Keywords: politics, emotions, Trump, Trumpism, right wing, left wing, progressive, populism, social movements, anger, hop
Perceived Benefits and Risks of Emotional Self-Disclosure in Therapy and Online Spaces
No full textEmotional self-disclosure, talking about one’s private thoughts and feelings, has been central to help-seeking and help-receiving since Freud. Previous research has explored how tendencies to self-disclose in therapy are influenced by personality traits such as shame, guilt, and attachment style; it has also suggested that client self-disclosure influences therapy outcomes. Relatively little research has explored how individuals conceptualize different “targets” of self-disclosure—that is, individuals or groups to whom they choose to reveal personal information.
Common targets of emotional self-disclosure have included individual and group therapy since their invention as interventions in the early 20th century, as well as family and other close relationships. More recently, anonymous and non-anonymous social media and conversational AI have gained attention as possible emotional self-disclosure targets. The present study examined how personality and demographic characteristics may be associated with emotional self-disclosure preferences. A previously validated therapy research measure was adapted and used to gather self-report data via MTurk from 671 participants about how rewarding and risky they would find emotionally self-disclosing towards different targets, including more relational targets such as therapists and family, and less relational targets such as anonymous social media and AI. Initial analysis revealed personality and demographic traits that contribute towards self-disclosure preferences. Further analysis segregated participants into three groups based on level of therapy experience: none, low, high. Latent profile analyses of these three groups revealed multiple psychologically distinct subclasses.
Findings suggested that preferences for self-disclosure targets are highly differentiated, with individuals weighing both psychological and social risk and reward factors in deciding where to self-disclose. Individuals high in trait shame and insecure attachment showed no preference for more relational targets over less relational targets. These “omni-disclosers” appeared in each therapy experience-level group, as did “relational-preferrers” who on average were less socially anxious and less ashamed. The results highlighted the context-sensitive nature of modern help-seeking and reflect a world in which engaging in emotional self-disclosure on anonymous social media and with conversational AI is increasingly normal, even among those engaged in other supports such as therapy. Directions for future research and applications for therapy training, digital intervention design and ethics, and public mental health outreach were discussed
Essays on Collaborative Innovation
No full textThis dissertation examines how technological and policy shifts transform collaboration ininnovation across three different contexts: healthcare, commercial space, and entrepreneurship.
Using large-scale empirical data and advanced econometric methods, I demonstrate how individuals and organizations can leverage technology and strategic partnerships to accelerate innovation while navigating complex market dynamics.
My first study analyzes the impact of adopting digitization technology on scientific collaboration among 313,257 researchers at 148 U.S. teaching hospitals. Exploiting the staggered implementation of Electronic Health Records (EHR) systems as a quasi-experimental shock, I find that digitization increases research collaboration by 6.1% and intellectual diversity of teams by 1.1%. The effect is 24% stronger than other traditional health information technology (IT) systems such as data management software, driven by improvements in data interpretability, and doubles when collaborators use the same EHR system because of increased data shareability. This study reveals how standardized IT infrastructure can unlock collaborative potential, with policy implications for the 3.8 trillion dollars U.S. healthcare industry and the innovation of medical Artificial Intelligence (AI) and machine learning (ML).
The second study assesses the outcomes of NASA’s commercial space initiative by analyzing the scientific and economic impacts of experiments carried out onboard the International Space Station (ISS). Using NASA’s launch records, I constructed a novel dataset of space experiments conducted between 2001 and 2021. Analyzing the publications and patents resulting from these experiments, I find that ISS-based research generates 63% more paper and 82% more patent citations than their digital twins—comparable experiments conducted in Earth labs—controlling for confounding factors such as the reputation of the principal investigator and the publication outlet. However, this impact diverges sharply between public and private research, with commercial experiments showing limited knowledge spillovers. These findings contribute to our understanding of how innovative organizations such as NASA, academic institutions, and high-tech companies responds to sudden shifts in technological and policy environments. They also inform the design of public-private partnerships in emerging industries, particularly as the space economy is projected to approach $500 billion by 2030.
My third study examines how high-tech entrepreneurs strategically position themselves for different exit outcomes. Two specific outcomes are considered—acquisition and Initial Public Offering (IPO), given that more than 90% of startup entrepreneurs exit through one of these channels. Applying ML methods on the U.S. federal SBIR program data, I developed an “orientation score” that isolates a firm’s positioning toward one outcome over the other while holding its underlying quality constant. My analyses reveal important variation in orientation associated with both the technological specialty of firms and the economic environment surrounding them. IT firms orient 40% more toward collaborative exits via acquisition, while biotech firms favor the more competitive path through IPO. Regional innovation ecosystems strongly predict these strategies, with local patenting activity increasing acquisition orientation by more than 30%.
Collectively, these three studies advance our understanding of how organizations adapt to technological disruption and policy change to create value through collaboration. The findings have immediate applications for healthcare systems implementing digital transformation, government agencies designing innovation programs, and investors evaluating startup strategies. My work demonstrates that successful innovation increasingly depends not on isolated brilliance but on the strategic orchestration of collaborative networks, a capability that determines competitive advantage across scientific domains and industries.
This dissertation contributes methodologically through the application of advanced econometric models, such as the staggered difference-in-differences estimators, to address treatment effect heterogeneity, the use of ML to resolve endogenous selection, and the construction of unique datasets linking innovation inputs to measurable outcomes. These approaches enable rigorous causal inference and prediction, allowing me to address important questions facing the production of scientific knowledge and the strategic dynamics within innovation ecosystems
Why Gig Platform Wage Theft is a Governance Crisis
No full textThe article argues that wage theft on gig platforms is not just a labor issue but a governance crisis driven by opaque algorithmic management that effectively acts as an unaccountable employer. It shows how major U.S. platforms deploy data-driven systems to set individualized wages, surveil workers, and algorithmically terminate them while hiding behind contractor misclassification and weak regulation. Contrasting a deregulatory U.S. trajectory under Trump with emerging European rules that restrict automated decisions and require human oversight, the piece calls for robust algorithmic governance laws, reclassification of gig workers with real protections, and international labor standards that treat algorithmic management squarely as a matter of workers’ rights and democratic accountability
Guías de Participación Comunitaria y Planificación para la Acción Climática Local
No full textEl cambio climático ya está generando impactos negativos palpables y los modelos climáticos muestran que, incluso en escenarios de fuertes reducciones de emisiones de gases de efecto invernadero (GEI), muchos impactos seguirán intensificándose en las próximas décadas debido a la inercia del sistema climático.
Ante este escenario, la adaptación y la resiliencia dejan de ser una opción para convertirse en una responsabilidad
ineludible. Las municipalidades y las comunidades locales están en la primera línea del riesgo climático, pero a
menudo carecen de recursos técnicos, financieros y políticos para planificar cómo enfrentarlo de forma oportuna y sostenida. Fortalecer la capacidad de los gobiernos locales para analizar riesgos y oportunidades, diseñar estrategias robustas, integrar conocimientos locales y visiones comunitarias, integrar el cambio climático en la gestión pública, y financiar, monitorear y evaluar la acción climática se vuelve, por tanto, una prioridad.
El presente documento reúne ocho guías metodológicas con herramientas prácticas para apoyar a los gobiernos locales en la planificación. Las guías se enfocan en la articulación de diferentes instancias de participación comunitaria (talleres comunitarios, entrevistas, encuestas) con el diagnóstico de riesgos climáticos y prioridades de acción, y el diseño de un Plan de Acción Climática local robusto. Su propósito es facilitar el paso desde la planificación hacia una implementación efectiva, transparente, con mejora continua, enfoque territorial e integración sectorial.
Está dirigido principalmente a equipos municipales y otros actores territoriales involucrados en el diseño e implementación de Planes de Acción Climática locales (en Chile, Planes de Acción Comunal de Cambio Climático, PACCC).
Cada guía ofrece un paso a paso, materiales complementarios de apoyo y recomendaciones para levantar información de calidad y traducirla en componentes concretos del plan. No constituyen un manual exhaustivo de todo el proceso de elaboración de un Plan de Acción Climática, sino que se enfocan en áreas priorizadas a partir del trabajo con los municipios de Alto del Carmen, Coquimbo y San José de Maipo, pero que pueden ser pertinentes para otros territorios. Las guías y sus materiales complementarios son adaptables al contexto de cada municipalidad; se recomienda ajustarlos para asegurar pertinencia cultural y coherencia con los marcos legales y procesos de planificación vigentes a nivel nacional, regional y comunal. Las guías pueden utilizarse de manera conjunta o por separado, según el interés y necesidad de cada municipio
Culturally Responsive Project-Based Science for Elementary School Girls of Color in an After-School Science Program
No full textThis dissertation presents a qualitative case study of four 4th-grade elementary school girls of Color1 who participate in a purposefully designed Culturally Responsive Project-Based Science (CRPBS) Afterschool Program. It explores their scientific inquiry-based learning processes and collaborative learning strategies as they engage in structured scientific inquiries to ultimately design and construct a STEM artifact, symbolizing an eco-friendly playground for their school community.
The goal of this study is to contribute to the limited research on how students’ scientific inquiry-based discourse and collaborative discourse in Culturally Responsive Project-Based Science shape their student group learning processes. Moreover, this study employs Culturally Responsive Pedagogy (Gay, 2018) and Culturally Relevant Teaching (Ladson-Billings, 2021) as frameworks. The data collected included the girls’ semi-structured entrance interviews, Likert-scale surveys measuring their levels of science interest, focus group discussions about their scientific inquiries, and the creation of their STEM artifacts. The girls conducted multiple scientific inquiries by engaging in the following activities: (A) Observation of a Documentary, (B) Scientific model analysis, (C) Reading and data analysis, (D) Experimentation, (E) Scientific-subject matter interviewing, and (F) Field study. They apply their understanding of the scientific evidence gathered to collaboratively design and construct a STEM artifact representing a sustainable playground.
These data were recorded, transcribed, and coded to identify emerging themes that informed interpretations of inquiry-based learning processes and collaborative learning strategies, as well as their impacts on the girls’ interest in science. The findings from this study have implications for culturally responsive and culturally relevant pedagogical practices in Project-Based Science, which can meaningfully improve the STEM identities of elementary school girls of Color.
Keywords: culturally responsive project-based science, culturally relevant teaching, girls of Color, scientific inquiry-based learning, collaborative learning strategies
1 The term girls of Color refer to Black, Asian, Indigenous, and Latina young girls (Amaka, 2024)
Behavioral and Psychological Influences on Physical Activity: Insights from Motivation States, Stress, and Real-Time Behavior
No full textTo better explain daily fluctuations in physical activity and sedentary behavior, investigations of motivation are turning from social cognitive frameworks to those centered on affect, emotion, and automaticity. This shift of investigation on motivation and physical activity shares a common idea that human behavior is driven strongly by desires and/or similar concepts of wants, urges, and cravings.
The purpose of this dissertation was (1) to review theoretical and empirical advances in understanding motivation states for physical activity, (2) to translate and validate the Korean version of the Cravings for Rest and Volitional Energy Expenditure (CRAVE) scale, and (3) to investigate real-time relationship between stress, motivation states, and objectively measured physical activity using ecological momentary assessment (EMA) and accelerometry.
Chapter 2 synthesized conceptual frameworks and empirical findings demonstrating that motivation for physical activity fluctuates dynamically within individuals and is influenced by affective, contextual, and physiological factors. Drawing on contemporary frameworks such as the WANT model (Wants and Aversions for Neuromuscular Tasks) (Stults-Kolehmainen et al., 2020a), the Affective-Reflective Theory (ART) of physical inactivity and exercise (Brand & Ekkekakis, 2018), and the Affective–Health Behavior Framework (AHBF) (Williams & Rhodes, 2023), this chapter discussed the conceptual evolution of motivation research in physical activity, emphasizing the transition from trait-based and cognitively oriented theories to dynamic perspectives that view motivation as situational, affectively influenced, and continuously fluctuating across time and context. It further underscored that the motivation to move and the motivation to rest are not opposite ends of a single continuum but rather represent interrelated yet distinct systems that jointly regulate spontaneous, moment-to-moment activity (Stults- Kolehmainen et al., 2021). The review also emphasized the value of state-sensitive and ecologically valid assessment tools, such as EMA and accelerometry, for capturing temporal variability in both motivation and behavior in naturalistic contexts (Crosley Lyons et al., 2023; Dunton, 2017). Together, these developments illustrate a paradigmatic shift toward viewing physical activity motivation as a dynamic, affectively charged process that unfolds in real time, bridging theoretical models with methodological innovation.
Chapter 3 evaluated the psychometric properties of the Korean-adapted CRAVE scale (CRAVE-K) among Korean-speaking adults. Following a rigorous, multi-phase translation and cultural adaptation process, the CRAVE-K was administered to 393 participants to assess its factorial validity, internal consistency, and construct validity across both the “Right Now” and “Past Week” forms. Exploratory and confirmatory factor analyses supported the original two-factor structure (Move and Rest) and demonstrated that the 10-item version provided superior model fit compared with the 13-item version. Reliability coefficients were excellent (α = .86– .93) across subscales and timeframes, confirming the internal consistency of the measure. The CRAVE-K demonstrated sound psychometric properties and appears to be a reliable and culturally appropriate instrument for assessing affectively charged motivation states for movement and rest among Korean-speaking adults.
Chapter 4 employed a seven-day EMA design to examine within-person associations among momentary stress, motivation states for physical activity, and accelerometer-measured behavior. Thirty-seven adults completed six smartphone-delivered EMA prompts per day while wearing ActiGraph wGT3X-BT devices in their free-living environments. Multilevel linear mixed-effects models indicated that higher momentary motivation to move was associated with greater engagement in both light-intensity (LPA) and moderate-to-vigorous physical activity (MVPA), as well as lower sedentary time. In contrast, momentary stress was not directly associated with physical activity outcomes. However, higher stress was associated with stronger motivation to rest, suggesting a potential motivational pathway through which stress may influence behavior. Both motivational states and activity patterns exhibited diurnal variation, with motivation to move and physical activity peaking earlier in the day and declining across the evening, while motivation to rest increased later in the day.
Collectively, these studies advance a comprehensive understanding of motivation states for physical activity as a dynamic, context-dependent process shaped by affective and situational factors. By integrating theoretical synthesis, psychometric validation, and real-time behavioral analysis, this dissertation provides an empirically grounded and methodologically rigorous foundation for advancing state-based models of motivation. These findings underscore the importance of capturing momentary experiences to understand and promote physical activity and inform the development of adaptive, precision-based interventions that align with individuals’ motivational readiness and contextual circumstances
Gene-Agnostic Metabolic Engineering in Inherited Retinal Degenerations
No full textInherited retinal degenerations (IRDs) culminate in non-cell-autonomous cone loss following rod failure and destabilization of outer-retinal metabolism. This dissertation tests whether compartment-specific metabolic reprogramming in rods, cones, and the retinal pigment epithelium (RPE) can preserve cone structure and function independent of genotype in etiologically diverse mouse models, including phosphodiesterase 6B (6), rhodopsin (), and membrane frizzled-related protein () mutant lines. The studies herein establish that metabolism can be therapeutically redirected across these compartments, supporting a strategy that complements gene-specific augmentation while extending protection to most patients without access to tailored genetic therapies. By reframing retinal degeneration as a disorder of metabolic ecosystem collapse, this work lays the conceptual and experimental foundation for therapies that are both mutation-agnostic and scalable, with potential relevance to common degenerative conditions such as age-related macular degeneration.
Aim 1 (rods – 1) used 6⌃(⁶²⁰/⁶²⁰) and ⌃(¹¹⁰/⁺) mice to modulate rod metabolism via conditional prolyl-hydroxylase disruption(PHD) [6-CreERT2] and dual-AAV CRISPR editing of 1. Outcomes combined electroretinography (ERG), optical coherence tomography (OCT), histology, cone flatmounts, lactate assays, and [U-¹³C]glucose tracing. PHD2 disruption induced a Warburg-like shift via enhanced ¹³C labeling of glycolytic intermediates with increased phospho-pyruvate dehydrogenase 1 (Ser293), without elevating bulk lactate, preserving cone morphology, and improving cone-mediated ERG across recessive and dominant models. One-year fluorescein angiography showed no neovascularization. These results demonstrate that rod-specific glycolytic reprogramming through PHD2 disruption preserves cones by stabilizing carbon flux and redox balance, establishing 1 as a dominant lever for mutation-agnostic metabolic rescue in murine rods.
Aim 2 (cones – 1) targeted cones in 6⌃(¹/¹) and 6⌃(⁶²⁰/⁶²⁰) mice with 1 deletion [3-CreERT2] and evaluated photopic ERG, cone flatmounts, and whole-retina qPCR/immunoblotting of canonical nuclear factor erythroid 2-related factor 2 (NRF2) targets. 1 loss yielded consistent structural rescue, higher opsin-positive cone counts, and healthier segment morphology. While functional gains were modest, bulk assays showed minimal induction of canonical NRF2 targets. This suggests cone protection may arise through subtle redox stabilization or structural preservation rather than broad transcriptional reprogramming, refining the paradigm of NRF2-mediated rescue.
Aim 3 (RPE – ) reprogrammed the RPE in ⌃(⁶/⁶) mice by deleting [65-CreERT2] and assessed longitudinal ERG/OCT, cone flatmounts, RPE whole-mount morphology, and [U-¹³C]palmitate tracing to -hydroxybutyrate (BHB). loss increased ¹³C-palmitate incorporation into BHB and qualitatively preserved RPE architecture, with a mid-course plateau in outer nuclear layer thinning, late-stage scotopic ERG improvements, and significant peripheral cone preservation. This indicates that mutation-agnostic RPE reprogramming can secondarily stabilize photoreceptors in the background of IRD.
These experiments demonstrate that tuning rod glycolysis, cone redox balance, and RPE fattyacid -oxidation provide complementary, mutation-agnostic protection of cone vision, establishing retinal metabolism as a practical therapeutic axis alongside gene-specific repair. By showing that interventions in distinct retinal compartments converge on the shared goal of cone preservation, this work reframes IRDs not only as collections of genetic defects but as disorders of metabolic interdependence. This perspective expands the therapeutic landscape beyond mutation-specific augmentation, highlighting metabolism as a scalable axis applicable across the genetically heterogeneous spectrum of IRDs. Moreover, by identifying nodal regulators that can be targeted with genetic or pharmacologic tools, these findings create a translational bridge to common late-stage diseases such as age-related macular degeneration, where metabolic instability is a central driver of pathology. Together, this dissertation establishes metabolism-centered therapy as a unifying framework that can complement precision medicine and reshape how retinal degeneration is treated in both rare and common contexts
Architecting Privacy as a Computing Resource in Data Aggregation Workloads: From Browser to Datacenter
No full textUser privacy is a critical resource consumed by modern aggregation-oriented data workloads, such as machine learning, yet unlike other system resources, it is rarely accounted for, tracked, or managed to ensure that its consumption does not harm users. Differential privacy (DP) offers strong theoretical foundations for tracking this resource and protecting user privacy, but adoption in practice remains limited.
This dissertation advances the thesis that DP's practicality and deployability can be substantially improved by following four principles. First, DP should be integrated into shared infrastructure such as browsers, databases, or datacenter platforms so that many applications can benefit, rather than relying on custom-built solutions as is common today. Second, privacy semantics should be well-defined and suitable for the system, since traditional DP semantics may not always fit. Third, design should be rooted in formal analyses that faithfully model system behavior, avoiding the misassessments that result from oversimplified models, as is customary today. Fourth, privacy should be treated as a first-class computing resource, drawing on resource management techniques from the systems literature, in addition to DP-specific methods, to address challenges such as budget exhaustion.
Applying these principles, we design, prototype, and evaluate three classes of DP systems, each demonstrating how they enhance DP's practicality and deployability. In the web domain, Cookie Monster introduces an efficient in-browser budgeting system for ad measurement APIs, leading to its adoption as the core privacy architecture of a W3C draft standard now supported by major browsers. Big Bird extends this foundation with quotas and fair scheduling to protect the shared privacy budget against adversarial depletion, showing how resource allocation techniques can defend privacy in adversarial environments and enable DP adoption at scale in browsers. In datacenter workloads, PrivateKube integrates privacy as a native resource in Kubernetes with a scheduler tailored to its unique properties, while DPack improves efficiency by maximizing task throughput under fixed privacy guarantees; together, they illustrate how formally grounded design and resource-aware scheduling can overcome the budget exhaustion barrier. Finally, in analytics workloads, Turbo transforms the theoretical private multiplicative weights algorithm into a practical caching layer for DP databases that reuses past results to reduce privacy cost and improve accuracy, demonstrating how systems techniques and DP-specific mechanisms can make previously impractical algorithms usable in practice.
Together, these contributions establish a principled foundation for architecting privacy into modern data aggregation workloads, from the browser to the datacenter. By treating privacy as a resource, embedding it into infrastructure, and tailoring privacy guarantees and analyses to systems realities, this dissertation demonstrates how a systems approach to privacy can help advance the practicality, robustness, and deployability of differential privacy
Construction Waste Recycling in Melbourne
No full textThe focus of this report is to investigate the problems surrounding the effective and efficient handling of construction site waste through the lens of recyclable waste management. This case study looks at the construction of an inner city, seven-storey High Rise Building project in Melbourne Australia – a city with a population of 5,100,000.
The purpose of this report is to have maximum impact on the reduction of construction demolition waste (CDW) material to disposal by rerouting this CDW material through the most effective and efficient secondary recycling process and identifying the high value waste materials in terms of commercial return and potential volumetric/weight. Adopting a triple bottom line approach provides commercial benefits to contractor/builder/client stakeholders and social benefits to the community at large, improving the environmental footprint and creating a sustainable construction industry for future generations