10:20 AM – 11:35 AM | Merten Hall, Rooms 1202 and 1203
Power, Policy, and Inequality
10:20am – 11:35am | Merten Hall, Room 1202
Discussant: Solon Simmons, Ph.D.
The Impact of U.S. Aid Suspension on Educational Advocacy and Operations for Afghans: A Call to Action for Social Workers
Wida Saber (College of Public Health)
In early 2025, the U.S. administration announced a 90-day suspension of foreign aid programs, including those managed by the U.S. Agency for International Development (USAID), to review their efficiency and alignment with foreign policy objectives (Reuters, 2025). This decision has severely impacted advocacy programs and educational operations in Afghanistan, where millions of children, especially girls, rely on international support to access schooling. Education is not a luxury but a fundamental human right, as recognized by the Universal Declaration of Human Rights (United Nations, 1948). The suspension of aid disproportionately affects the most vulnerable populations, reinforcing systemic barriers to education and increasing risks of child labor, early marriage, and long-term poverty. Instead of completely cutting off funding, a more effective approach would involve targeted reforms that ensure aid is spent efficiently while still prioritizing education. Transparent financial oversight, community-led initiatives, and direct investment in independent educational organizations can help mitigate the harm caused by broad funding freezes. This crisis presents an urgent call to action for social workers, who play a vital role in advocating for children’s rights, supporting displaced families, and working with international organizations to ensure continued educational access. Social workers must engage in policy advocacy and collaborate with humanitarian agencies. This presentation will analyze how the aid suspension disrupts advocacy efforts, hinders educational access, and threatens. It will also highlight potential policy reforms that would allow continued support while addressing concerns over aid effectiveness. The international community, including social workers, must recognize that investing in education is not merely an act of charity but a necessary step toward stability and human rights.
Wage Gaps and Class Gaps: Redressing the Structural Subjugation and Undervaluation of Feminized Care and Domestic Labor in the Contemporary U.S.
Aziza Bayou (College of Humanities and Social Sciences)
How are commodified forms of care and domestic labor valued and compensated in the U.S. at the current conjuncture? How does this relate to the ongoing gendered wage gap? This paper utilizes a Marxist feminist analytic framework to examine current mainline economic analyses of the U.S.’s gendered wage gap to explicate the devaluation of feminized labor forms. I argue that the professional middle class’s reliance on feminized care and domestic wage labor is a structurally necessary precondition to the reproduction and accumulation of capital within the current gendered division of labor. I contend that mainline political economic analyses of the U.S.’s gendered pay gap neglect the requisite subordination of these essential labor forms by singularly focusing on the wage gap between “commensurate” women’s and men’s labor, which has been the benchmark for extant law and policy. I posit that the dialectical relationship between the relatively low exchange value of these feminized labor forms in contrast with non-feminized professions is a central barrier to gender and class equity, and that the household burden of wage payment directly produces the marginalization and subordination of feminized labor. I discuss standpoints on the racialized and kinship-reproduced elements of class construction in contributing to this hierarchical division of labor, and I consider proposals to ameliorate these inequities via redistributive subsidy measures, including Nancy Fraser’s proposed “universal caregiver wage.” This work attempts to heed Martha Gimenez’s directive to focus on working-class labor as a central site of contestation in service of equitable change.
Is Trump a Fascist?: Comparing the Moral Grammar of Trumpism to Conservatism, Nationalism, and Fascism
Oakley Hill (Carter School for Peace and Conflict Resolution)
Over the last decade, the U.S. Republican Party has seen a radical ideological transformation from the neo-conservative imaginations of John McCain and Mitt Romney, to that of Donald Trump. However, it has not been clear what Trumpism is nor how we should classify it. Republicans refer to Trump as a “conservative,” while his opponents use the terms “populist,” “authoritarian,” and “fascist.” This study compares the justice languages and root narrative structure of Trump’s Campaign Speeches to the representations of liberalism, conservatism, socialism, nationalism, feminism, anarchism, and fascism found in Festenstein and Kenny’s (2005) “Political Ideologies,” a reader published by Oxford. Unlike comparative theories, root narrative theory accounts for the four basic abstractions we use to imagine political situations (i.e., the antagonist abuses power to create injustice for the protagonist) and the political values at stake in those situations (i.e., security for the state, liberty for the individual, equality for the people, and dignity for the other). This study identifies Trump’s ideological relatives based on their common imagination of the political situation–i.e., a situation in which the attainment of the same political value is blocked by the same types of antagonists who abuse the same types of power to create the same types of injustices for the same types of protagonists.
The Climate Crisis Is Here, but Is the U.S. Government Ready? Lessons from LA and Florida
Yenting Lin (Schar School of Policy and Government )
The 2025 Los Angeles wildfires and 2024 Florida back-to-back hurricanes have exposed weaknesses in the U.S. government’s disaster preparedness, emergency response, and long-term recovery efforts. This study examines whether U.S. climate policies sufficiently protect vulnerable communities or if political inaction, misinformation, and polarization have left the country unprepared. This study employs case study analysis of the 2025 LA wildfires and 2024 Florida hurricanes, evaluating government response through reports, media coverage, and policy analysis. A comparative policy analysis contrasts U.S. disaster management with international best practices, particularly the Netherlands’ flood resilience system.Findings suggest insufficient preparedness, slow response times, and unequal resource distribution, with low-income communities suffering prolonged displacement. While federal initiatives like the Inflation Reduction Act and FEMA’s relief programs exist, they lack strategies. In contrast, the Netherlands invests in climate adaptation infrastructure, flood-resistant urban planning, and community-led resilience programs. This research argues that the U.S. remains reactive rather than proactive in addressing climate crises. To improve resilience, the U.S. should adopt: flood management models like the Netherlands; community-led adaptation programs; mandatory climate planning across all government levels. Without urgent reforms, the U.S. risks falling behind in climate adaptation, leaving millions unprotected.
Censorship in Technological and Social Spaces
10:20am – 11:35am | Merten Hall, Room 1203
Discussant: Alan Shark, Ph.D.
Bias Detection and Mitigation in Zero-Shot Spam Classification using LLMs
Hossein Salemi (College of Engineering and Computing)
There is a growing number of scams through various communication mediums, including social media, phone calls and messages, search engine advertising, etc. Often these scams are realized via sending spam texts on any of these communication mediums, and therefore, prior research has investigated the task of spam classification to design information filtering systems. However, existing works have explored supervised machine learning techniques primarily, which suffer from the bottleneck of requiring large labeled datasets. Further, the studies are based on platform-specific data and lack critical analyses of biases in the predictive modeling behavior. In this work, we propose a zero-shot spam classification task, which does not require any labeled data for model training in an unseen domain. We propose a novel method to leverage state-of-the-art large language models (LLMs) in natural language processing (NLP) for this task. We employ this method to analyze zero-shot performance on spam datasets across two communication platforms (YouTube and phone messages) while mitigating biases in the model behavior. Our experimental results demonstrate the strong performance of a LLM-based zero-shot classifier with a goal-oriented prompting strategy. The resulting classifier is platform-agnostic, shows less bias towards data with certain behavioral attributes (e.g., spam content with sadness emotion), and is efficient in minimizing both false positive and false negative errors. The application of this research can inform effective spam filtering systems to prevent scams prevalent in different communication media ultimately.
Do Predictive Models Misclassify Autistic Writing as AI-Generated?
Summer Chambers (College of Humanities and Social Sciences)
Recent literature suggests that AI-detection models cannot accurately identify AI-generated text and may falsely classify non-native English speakers’ writing as AI at disproportionate rates. In the present study, anecdotal claims that autistic writers more often have their work flagged as AI-generated are examined empirically. A corpus of ~60,000 Reddit posts split into “likely-autistic” and “general-reddit” subcorpora is used to compare the distribution of probabilities output by the OpenAI GPT-2 detection model. Results showed that while less than two percent of either subcorpus was flagged as AI-generated by the model, significantly more texts from the “likely-autistic” subcorpus were flagged. The widespread use of AI-detection models with such a bias in their output prompts ethical scrutiny. Further research is needed to understand what features of autistic authors’ writing (and what features of AI-generated text) might contribute to this bias. Subsequent experiments incorporating newer AI-detection models and datasets with more objectively matched subcorpora may help to clarify the effect that autistic writing styles have on the prediction outputs of AI-detection models.
Regulated Behind the Wall: Public Toilets as Social Objects Embody Culture
Yang Hai (College of Humanities and Social Sciences)
The research examines public toilets, where contradictions coexist, as social objects, and their intersection with legal regulations, culture, and the public. Public toilets, along with other social objects, are key parts of the urban environment and society. The research on social objects, however, was undervalued by classical sociology. Existing studies on public toilets, from the perspective of culture, tend to be culturally deterministic, and lacking dynamism. How do public toilets embody various cultural characteristics and correspondingly influence human behaviors and their understanding of idea such as sex and privacy? From the cultural research approach, I qualitatively analyze how public toilets, where the boundary between frontstage and backstage is blurred, relate to broad cultural contexts, especially sex and privacy. Doing so uncovers the interrelated nature of public toilets, culture, and social behavior. Furthermore, crises of trust arising from cultural changes and lack of consensus will also be explored as a related microcosm of our changing world.