Maryland Shared Open Access Repository

MD-SOAR is a shared digital repository platform for twelve colleges and universities in Maryland. It is currently funded by the University System of Maryland and Affiliated Institutions (USMAI) Library Consortium (usmai.org) and other participating partner institutions. MD-SOAR is jointly governed by all participating libraries, who have agreed to share policies and practices that are necessary and appropriate for the shared platform. Within this broad framework, each library provides customized repository services and collections that meet local institutional needs. Please follow the links below to learn more about each library's repository services and collections.

 

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An Introduction to Ethnographic Literary Craft: A Decolonizing Approach to Interdisciplinary Work
(2024-12-09) Jennifer Tehani Sarreal; MFA in Creative Nonfiction
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How Partisanship & Political Ideology Influences Empathy and Tolerance
(2025-04-23) Todd, Olivia; Dr. Tamelyn Tucker-Worgs, Associate Profesor of Poltical Science at Hood College; Dr. Carin Robinson, Associate Professor of Political Science at Hood College; Alan Goldebbach, Associate Professor of Communication Arts and Journalism; Hood College Political Science; Hood College Departmental Honors
Decades of previous research suggest that liberals tend to be more empathetic and tolerant than conservatives, with scholars attributing these measurable gaps to the differing core values of each ideology. More recent studies challenge these notions, finding that liberals and conservatives have similar levels of tolerance and empathy when exposed to ideological outgroups. This study sought to find if empathy was a predictor for tolerance by replicating the ideological-conflict hypothesis, which is that both liberals and conservatives share a tendency to be intolerant towards groups they perceive as ideologically opposed. In this study, 177 participants that were 18 years or older from the U.S. responded to items from the Empathy Assessment Index (EAI) to assess participants’ empathy levels. Then, participants were asked to rank from a sample of 10 groups from most-liked to least-liked. Afterward, they were asked how willing they are to allow their least-liked group the right to political participation and other items measuring political tolerance. This study hypothesized that self-identified liberal participants would respond more positively to the empathy related questions than self-identified conservatives and that members from both ideologies will have similar levels of intolerance toward ideological outgroups. Although liberals and conservatives selected least-liked groups that were ideologically dissimilar, the results of this study contrast from previous research in tolerance and empathy. Correlational analyses revealed no partisan difference in responses to the Empathy Assessment Index and liberals were found to be less tolerant than conservatives. Further research is needed in these areas for a more complete understanding of how to capture empathy and tolerance levels as the current political climate may compromise the reliability of these measures.
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Utilizing Latent Space Representation for Disease Phenotyping and Patient Risk Stratification
(2025-04-25) Ferrer, Sophia Isabel; Dr. Aijuan Dong; Hood College Computer Science and Information Technology; Hood College Departmental Honors
Obstructive sleep apnea (OSA) is a common sleep-related disorder characterized by intermittent breathing pauses during sleep, which can significantly increase the risk of cardiovascular and metabolic diseases. The often undiagnosed nature of OSA, coupled with the difficulty in identifying patients most at risk for associated comorbidities, has led to sub-optimal personalized patient care. While previous studies have established a correlation between OSA and various comorbidities, the complexity and inconsistency of clinical data in electronic health records (EHR) pose challenges in deriving reliable results in healthcare studies. In this paper, we extracted and compared learned latent spaces-- a compressed representation of input data used to uncover hidden patterns-- using methods such as such as Autoencoders, Uniform Manifold Approximation and Projection (UMAP) and Principal Component Analysis (PCA) to filter out the noise and irrelevant details from the EHR data. We then deep phenotyped OSA patients through unsupervised clustering using the latent representation, identified patient subgroups and uncover potential risk factors that drive subgroup differentiation, and developed a clinical tool to predict patient group assignment via supervised learning. These findings enhance the understanding of OSA deep phenotyping and improve patient comorbidity risk assessment.
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Generative AI and User-Generated Content: Influences on Brand-Consumer Relationships and Purchase Intentions
(2025-04-24) Ghobrial, Meray; Witherow Brooke; Hood College English and Communication Arts; Hood College Departmental Honors
Recent advancements in generative artificial intelligence (genAI) applications have led to a growing body of research on organizational outcomes for companies that take advantage of these tools. Spending on AI in the retail sector and others has surpassed expert predictions, with expenditures on AI in marketing expected to reach $107.5 billion by 2028 (Ameen et al., 2021; Kshetri et al., 2024). As the use of AI in this field grows in popularity, especially among mid-level and junior marketers, marketing is predicted to be the firm function most heavily impacted by genAI (Cillo & Rubera, 2024; Kshetri et al., 2024). While the use of GenAI in marketing practices can be effective and efficient for organizations, research suggests this may come at a price (Bynder, 2024; Galloway & Swiatek, 2018; Xu et al., 2024). As AI becomes more advanced and AI and human-made content becomes less distinguishable, it is also more important than ever to study the impact of transparency on consumer-brand relationships. The purpose of this study is to examine the impact of genAI use in branded video content, specifically user-generated content, on brand-consumer relationship outcomes and customer buying intentions. The online survey experiment uses A/B testing-style procedures to analyze whether consumers react differently to brands that use AI than brands that do not, and the role of transparency in this relationship. The experiment found no significant impact of AI or disclosure on brand trust and satisfaction, but instead found that consumers could not easily distinguish between AI-generated and human-made content, emphasizing the importance of clear and transparent communications. This research is important because as genAI makes its way into video marketing content, it is critical to anticipate how consumers will react to it as it may potentially damage relationships with the brand.
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Music as Medicine: The Healing Quality of Music through a Historical, Cultural, and Modern Lens
(2025-04-25) Bechtel, Dominic; Dr. Noel Verzosa; Dr. Shannon Kundey; Dr. Sangeeta Gupta; Hood College Music and Performing Arts and Hood College Psychology and Counseling; Hood College Departmental Honors
Explores the marriage between music and medicine from a historical, cultural, and modern perspective.