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Augusto Boal’s Forum Theater in Language and Culture Courses A Reflective Documentation
No full textThis documentation is the revised and translated version of the 2024 article Augusto Boals Forumtheater im Deutschunterricht (Eikel-Pohen 2024). It features new observations from the second iteration of a Forum Theater project in a GER 202 language and culture class from 2025 and is again based on autoethnographic notes (Rodriguez-Mejia 2023: 71). It documents the revised application of Augusto Boal’s Forum Theater applied to language and cultural teaching in a socially critical, holistic, cooperative, intercultural, and practical way. Boal’s method aims at exposing structural oppression and allows individuals to explore options for action through play. The instructor followed Boal’s model when it aligned with language pedagogy and modified it without compromising its goals where it did not. This documentation does not claim to present formal research but a reflection on teaching practices at the intersection of intercultural language education (Wagner 2019: 9-13) and critical theater pedagogy. It focuses specifically on the value of reflections, gives examples of reflective methods, and delineates reflection practices under formative and summative assessment
What Municipalities Really Want: Perceptions of Artificial Intelligence among New York State Municipal Leaders
No full textThis brief summarizes What Municipalities Really Want: Perceptions of Artificial Intelligence among New York State Municipal Leaders, co-authored by Nicholas Croce (Syracuse University) & Saba Siddiki (Syracuse University)
Debt Accumulation in Fossil Fuel-Reliant Localities During the Shale Boom and Implications for the Energy Transition
No full textThe energy transition away from fossil fuels poses long-term economic risks for communities reliant on oil and natural gas production and some may face exceptionally difficult challenges in adapting to the change. Bonds issued by these localities during the shale boom may undermine their financial resilience as shale activity dwindles. Declining tax revenue may undermine their ability to service the debt and maintain the associated physical assets. This study addresses the extent to which local governments in oil- and natural gas-producing communities accumulated significantly more municipal debt during the shale boom than other communities. The analysis uses bond issuance data from the Mergent BondViewer database, a triple differences design, and county groups constructed using U.S. Bureau of Economic Analysis and Energy Information Administration data. The results show that counties exposed to the shale boom in 2008 through 2011 accumulated 26 percent more population-normalized outstanding debt compared with the 2004 mean. This is a statistically and economically significant increase that potentially warrants policy intervention. Moreover, the results suggest that policy interventions must address local governments beyond cities, towns, and counties, such as school districts and water utilities, and must accommodate significant heterogeneity among communities
SUPERVISED DIMENSIONALITY REDUCTION TECHNIQUES FOR UNDERSAMPLED APPLICATIONS
No full textSupervised dimensionality reduction (SDR) is a critical field in machine learning. The ability to compress data with high dimensionality can lead to better data visualization, pattern recognition, and classification performance. In order to develop SDR algorithms with the widest possible applicability, it is imperative to formulate approaches based on simple, generalizable models. The general Gaussian assumption provides a model that allows for closed-form expressions of information-theoretical criteria which can lead to analytical SDR solutions. Since Gaussian populations are completely defined by their first and second moments, solutions for this model can also lead to simple projection schemes with interpretable results that generalize well outside of their assumed framework. The first part of this thesis develops two linear projection methods to maximize the Kullback- Leibler divergence (KLD) under the general Gaussian model for the binary classification problem. Each method caters to a different parameter regime. One method is devised for the case in which the class means dominate the differentiation between the distributions. The other method focuses on the situation where the covariance dissimilarity provides most of the discrimination information. The second part of this thesis focuses on addressing the current limitations of existing SDR methods for binary classification. In particular, the asymmetric treatment of the two classes and the inability to handle under-sampled datasets are addressed in two novel algorithms developed under a max-min framework. The first algorithm optimizes over the KLD, balancing both the forward and reverse KLD to improve performance. The second algorithm seeks to extremize the class variances and is shown to be well suited for undersampled scenarios
A SYSTEMATIC REVIEW OF PARTICIPANT ADHERENCE TO WRITING SELF-MONITORING INTERVENTIONS
No full textWriting is a highly versatile tool that can be used to achieve various goals and serves as an effective means for learning (Bangert-Drowns et al., 2004; Graham, 2007). One widely adopted intervention to enhance student writing is self-graphing, which enables students to compare their current performance with previous efforts, thereby potentially increasing their motivation to improve subsequent performances (Harris et al., 1994; Hirsch et al., 2013; Sheehey et al., 2016; Wells et al., 2017). Self-graphing interventions require students to engage in self-directed self-assessments, which presents opportunities for noncompliance or errors. This necessitates the examination of participant adherence, or the likelihood of the participant to adhere to the intervention as intended. The current systematic review synthesizes the existing literature on self-graphing interventions in the writing domain among school-age students and provides a thorough examination of participant adherence in this context. Twenty experimental studies were reviewed to explore key aspects of participant adherence (e.g., reporting, measuring, timing, independence, associated research design, interaction with intervention outcomes). The findings of this systematic review suggest that very few studies have measured participant adherence, and even fewer report adherence outcomes. This study highlights a gap in the literature regarding the current conceptualization of treatment fidelity, underscoring the need for further examination of participant adherence and its role in intervention outcomes
Essays on the health and labor market effects of menopause and menopause hormone therapy
No full textThis dissertation explores how menopause and its medical management affect women’s health and labor market outcomes. Despite the centrality of reproductive transitions in the life course of women, economic research has largely ignored menopause, a biologically and socially significant milestone marking the end of fertility. This omission is surprising, given well-established medical evidence that links menopause to increased risks of health conditions that may impair the ability to work. The dissertation addresses this gap through two chapters that examine the causal impact of menopause and Menopause Hormone Therapy (MHT) on health and labor supply using two distinct empirical strategies. The first chapter uses genetic variation in menopause timing as an instrument to estimate the effects of natural menopause on health and employment. Leveraging polygenic scores (PGS) for age at menopause constructed from genome-wide association studies (GWAS) in the Health and Retirement Study (HRS), this chapter estimates an instrumental variables (IV) model that isolates the impact of menopause from age-related and behavioral confounders, among other empirical challenges. The results show that menopause triggers a significant deterioration in health. This health shock translates into a marked decline in full-time employment among post-menopausal women. The IV estimates suggest that acquiring an additional menopause-induced health condition reduces the likelihood of working for pay by 49 to 77 percentage points. Despite the richness of the HRS, it does not include information on MHT use, a widely used treatment to mitigate menopause-related symptoms. To further explore this issue, the second chapter studies the role of MHT, popularly known as hormone replacement therapy (HRT), on health and labor market outcomes. Using the abrupt drop in MHT prescriptions following the 2002 public release of the Women’s Health Initiative (WHI) trial results as a quasi-experimental shock, this chapter estimates the causal effect of MHT use using a difference-in-differences and instrumental variables framework applied to nationally representative data from the Medical Expenditure Panel Survey (MEPS) and the Current Population Survey (CPS). The results show that MHT improves physical health significantly, raising physical health scores by half to a full standard deviation. These health effects, however, do not translate into large or robust changes in employment or wages. Across multiple specifications, there is no strong evidence that MHT use significantly affects labor force participation or earnings
A HIERATICAL CONTROL FRAMEWORK FOR ASSESSING EV’S IMPACT ON RESIDENTIAL ENERGY FLEXIBILITY
No full textWith the development of the power grid, the world\u27s industries and economies have experienced a significant boost over the past centuries. However, as the demand for electricity from the grid continues to grow, utility markets face increasingly challenging tasks in balancing supply and demand. Additionally, integrating renewable energy sources such as solar, hydroelectric, and wind power into the grid adds further complexities to maintaining grid stability. In the United States, buildings account for about 76% of electricity consumption and 40% of greenhouse gas emissions, making them a major contributor to peak demand, which is nearly 80%. Therefore, buildings play a key role in enabling energy flexibility within the grid. Today, with the adoption of distributed energy resources (DERs) like photovoltaic (PV) systems, combined heat and power (CHP) units, and electric vehicles (EVs), buildings have become even better equipped to enhance energy flexibility. Notably, EVs have gained recognition for their ability to provide benefits to grid flexibility as their market share increases. However, due to a lack of data, there is a shortage of empirical evidence characterizing how EVs will impact energy flexibility. Also, there is no effective solution to better control the EV operations to fully unlock the potential of EVs to achieve energy flexibility. To fill in these gaps, this study examines the effects of EV charging and discharging behaviors by conducting a comprehensive analysis and optimization on a smart meter dataset, then expanding to an urban-scale simulation study through a data-driven approach for load data generation. It hypothesizes that an advanced hieratical control framework designs a personalized pricing structure, taking into account of diverse occupancy behaviors, can optimally regulate EV charging and discharging behaviors, while simultaneously providing grid services as peak load reduction, increasing grid profitability, reducing overall CO_2 emission, and lowering utility costs for users. Through data processing to understand diverse EV charging behaviors and occupancy schedules, this study developed a hierarchical control framework incorporating mutual optimization of EV charging/discharging scheduling and dynamic pricing. This empirical approach quantifies EV potential for enhancing residential grid flexibility while considering economic, environmental, and grid stability impacts. The study analyzed a smart meter dataset of 225 residential EV customers from Salt River Project (SRP) in Phoenix, Arizona. This community-scale analysis provided proof-of-concept validation before scaling up using a Conditional Generative Adversarial Network (cGAN) to generate realistic building load profiles replicating measured data distributions for urban-scale simulation. The same control framework and data processing procedure follows the same as community-scale study. Community-scale results demonstrated the framework\u27s effectiveness across various control objectives, achieving daily averages of: 22.3% peak load reduction, 2.1% decrease in user electricity bills, 21.3% reduction in utility generation costs, 2.0% grid profitability increase, and 1.7 metric tons CO2 emission reductions. Urban-scale simulation revealed enhanced performance with 28.1% peak load reduction, 0.7% user bill reduction, 6.3% utility generation cost reduction, 1.3% grid profitability increase, and 1.3 metric tons CO2 reduction. This research contributes to understanding: 1) empirical evidence of EV flexibility potential from comprehensive smart meter data; 2) smart charging/discharging strategies incorporating user behavior insights; 3) innovative pricing schemas enabling energy flexibility while accounting for EVs\u27 unique grid role; and 4) scaling findings from measured data to urban-scale simulations exploring EV impact on grid stress relief