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Empowering Families to Prevent Violent Extremism
Get PDFFamilies play a critical role in the pathways to radicalization and the prevention of violent extremism. While adverse family conditions — such as abuse, neglect, and parental abandonment — can increase vulnerability to radicalization, strong family bonds and emotional support can help individuals disengage from extremism and reintegrate into society. This study aims to understand the lived experiences of families impacted by violent extremism to identify how they can support prevention efforts. This research was conducted for policymakers, community organizations, and practitioners seeking to develop family-centered strategies to prevent radicalization and support deradicalization. By equipping families with knowledge, resources, and resilience-building tools, this work informs public health approaches that reduce reliance on punitive measures and foster community-based solutions
Sabar Bonda (Cactus Pears)
Get PDFThis is a film review of Sabar Bonda (Cactus Pears) (2025), directed by Rohan Parashuram Kanawade
The Participation Gap: Inequitable Speaking Times in Majority Women Classes
No full textThis study examined the participation of men and women in majority women classes. The participation of men and women was recorded in three majority women communication classes from August through October. It was found that even in majority women classes and majors, men still participate and interrupt disproportionately more. This result raises important questions about the continued presence of the chilly climate in the university setting- even in areas traditionally dominated by women. Equitable participation in the university classroom still has a ways to go, but professors may well be the answer to this problem (Farago & Flora, 2022). Prompting participation from women and curtailing excess participation from men promotes an equitable space for all students
INDIVIDUAL CONFIDENCE TO COLLECTIVE STRENGTH: ADVANCING NEBRASKA READING ACHIEVEMENT THROUGH EFFICACY
No full textThe University of Nebraska Omaha Early Literacy Workshop aims to train 7,000 teachers in the “Science of Reading” by 2030, strengthening Nebraska educators’ knowledge and skills in evidence-based literacy instruction. This study examines the relationship between full participation in the five-session workshop and teachers’ self-reported efficacy. A total of 356 educators completed Pre and Post Surveys, enabling analysis of growth in knowledge and practice and highlighting sessions with the greatest impact. Open-ended responses further revealed areas of development. A distinctive feature of this model is its flexibility. Content was adapted to district curricula, demographics, and local needs, ensuring a responsive, differentiated experience. As schools nationwide move toward instructional practices recommended by the National Reading Panel (2000), this workshop provides a promising model for professional development that is both meaningful and evidence driven
CONTENT ANALYSIS OF K-12 BEHAVIORAL THREAT ASSESSMENT AND MANAGEMENT (BTAM) GUIDES: IMPLEMENTING RESPONSES TO EXOGENOUS SHOCKS
No full textWhat information about teams, case management, reporting, information sharing, and training guidance are included in publicly available K-12 Behavioral Threat Assessment and Management (BTAM) guides? How does this information differ between K-12 BTAM guides created proactively versus those created reactively in response to an exogenous shock? In this thesis, I explore these questions by analyzing 30 publicly available K-12 BTAM guides. This study contributes to the sparse criminal justice literature on the use of BTAM guides, notably increasing the literature on BTAM responses to exogenous shocks. Findings indicate that although general information is usually included, specific details are often lacking. Further, important components like length of training are overlooked
IMPROVING THE UNDERSTANDING OF PRIVACY POLICIES USING LARGE LANGUAGE MODELS (LLMS)
No full textThis thesis explores the application of Large Language Models (LLMs) to improve the generation, classification, and accessibility of privacy policy content. As frameworks such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) evolve, organizations face growing pressure to present transparent, user-friendly privacy policies. However, the complexity of legal language and the variability of policy structures pose challenges for traditional analysis methods. This research addresses these issues using the LLaMA 2 open-source models developed by Meta. The study centers on two key investigations. The first evaluates the base LLaMA 2 model (zero-shot) and its 4-bit, 5-bit, and 8-bit quantized versions for generating coherent, legally sound privacy policy text for IoT applications. Outputs are assessed using ROUGE- LSum, BERT Precision Score, Word2Vec, and GloVe to measure fluency, contextual relevance, and regulatory alignment. Findings highlight trade-offs between model efficiency and output quality, demonstrating the potential of quantized models in resource-constrained environments. The second case study addresses sentence-level multi-label classification using the OPP- 115 dataset, which includes web-based privacy policies from diverse domains. It evaluates the performance of base, fine-tuned, and quantized fine-tuned LLaMA 2 models in cate- gorizing individual privacy statements into key regulatory categories—such as First Party Collection/Use, Third Party Sharing/Collection, and Data Retention—among others. Fine- tuning significantly improves classification performance, while quantized fine-tuned models achieve comparable accuracy with lower computational demands. Additionally, an inter- active AI-powered chatbot is developed using the fine-tuned model, allowing users to submit privacy policy text and receive categorized outputs along with simplified explanations, thereby enhancing user comprehension. This research demonstrates how LLaMA 2 models—particularly when fine-tuned and quantized—can support scalable, interpretable, and user-centered privacy policy automation. It offers practical insights for AI developers, privacy professionals, and policymakers while laying the groundwork for future work on hybrid approaches that combine model compression, fine-tuning, and conversational interfaces for privacy and digital rights communication
IDEOLOGY IS BRAT: POPULAR MUSIC AS DISCOURSE IN THE 2024 U.S. PRESIDENTIAL ELECTION
No full textThis thesis examines how popular music served as an ideological and hegemonic instrument during the 2024 United States presidential election. Drawing on the theoretical frameworks of Antonio Gramsci (cultural hegemony), Louis Althusser (ideology and interpellation), Lawrence Grossberg (the duality of music), Michel Foucault (discourse and power), and Theodor Adorno and Max Horkheimer (popular culture as mass deception), this study explores how political campaigns utilized music to construct, reinforce, and challenge dominant ideological narratives.
The analysis begins with Kamala Harris’s entry into the race following Charli XCX’s viral “Kamala is Brat” tweet, signaling a cultural repositioning. It traces the use of Beyoncé’s Freedom as Harris\u27s campaign theme, Donald Trump’s continued embrace of God Bless the USA, and the Democratic selection of Tim Walz as vice-presidential nominee, which introduced a populist tone. Chapter three investigates the 2024 Republican National Convention, highlighting performances by Lee Greenwood and Kid Rock that emphasized American exceptionalism, Christianity, and resistance to perceived liberal dominance. Chapter four turns to the 2024 Democratic National Convention, analyzing how the Harris campaign used music and messaging to define freedom and patriotism in ways aligned with progress, hope, diversity, and inclusivity. Finally, chapter five explores the final weeks of the campaign, when the Village People\u27s Y.M.C.A. became closely associated with the Republican campaign, symbolizing a populist reclaiming of public celebration and communal identity.
Ultimately, this thesis argues that the 2024 election cycle marked an unprecedented intensification of popular music as a hegemonic force, offering campaigns a unique cultural medium to interpellate voters, shape political discourse, and solidify ideological identities in an era of heightened cultural fragmentation. At a time when political identities have become increasingly performed through aesthetics rather than policy, the study of campaign music is more critical than ever
