Minnesota State University, Mankato
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Crip negativity [book review]
Get PDFThis is a tiny book with a lot packed into it. Smilges is a neuroqueer author and shares personal experiences entwined with disability and queer theories to talk about the value of negativity
Introducing Zippy: Digitizing Our Collections One Clip at a Time
Get PDFThe University Archives and Southern Minnesota Historical Center at Minnesota State University, Mankato recently purchased a large format overhead scanner from the Crowley Company. We’d like to share what we purchased and why, and how Zippy (yes, we named our scanner) has changed our digitization possibilities and workflows
Sibling Bullying Reported by Emerging Adults: Profiling the Prevalence, Roles, and Forms in a Cross-Country Investigation
Get PDFPrevalence estimates of sibling bullying indicate it occurs more frequently and with more negative consequences than peer bullying, yet many countries do not track or investigate the phenomenon. University students from Argentina, Estonia, and the United States were surveyed to investigate their retrospective experiences involving sibling bullying, how often it occurred, the roles held, and the forms communicated. In the aggregated data, roughly 50 % of the sampled emerging adults (N = 3477) reported experience with sibling bullying, with the dual role of bully-victim being the most frequently reported role held by males and females, with the second role being bully for males and victim for females. Verbal forms of bullying were most frequently reported by males and females, with physical, relational, and technological forms occurring less frequently, indicating the importance of studying the messages conveyed during bullying incidents. Variations between biological sex, bullying role and form were detected that indicate siblings experience bullying in ways that are unique from peer bullying. Country comparisons revealed bullying frequencies varied among males and females, suggesting sibling bullying experiences are likely to be culturally influenced. More research is warranted to examine the negative impact bullying has on sibling psycho-social development and the potential transfer to non-familial relationships and contexts. Discussion of these findings and the implications for academics and practitioners alike is provided
November 2024 Native American Heritage Month Celebration
Get PDFBibliography and photographs of a display of government documents from Minnesota State University, Mankato.https://cornerstone.lib.mnsu.edu/lib-services-govdoc-display-ethnic/1020/thumbnail.jp
Living as LGBTQ+
No full textA social studies title for young adults that examines the history of the lesbian, gay, bisexual, transgender, queer, and more (LGBTQ+) community in the United States of America. Includes sidebars, real-person profiles, a glossary, a timeline, and further resources.
Part of the Living in America series
Ages 10 - 14https://cornerstone.lib.mnsu.edu/university-archives-msu-authors/1496/thumbnail.jp
DIY: The Wonderfully Weird History and Science of Masturbation
No full textAn approachable and humorous deep dive into the history, culture, and science behind masturbation.https://cornerstone.lib.mnsu.edu/university-archives-msu-authors/1470/thumbnail.jp
Side Effects May Very
No full textSide Effects May Vary is a memoir in fragments, coming together to tell the story of one woman’s journey with anxiety as she investigates its role in her family, relationships, and sense of self. Combined with research that highlights the role of women, anxiety, and society, Abigail Reed brings to light the pressures women face in their lives, relationships, and in their health. A mix of personal narrative, research, and lyrical prose, Side Effects May Vary will leave the reader with a sharp impression of what it means to be a woman in today’s anxious society
Data Visualization, Licensing, and other Generative AI Initiatives at Minnesota State University Mankato
Get PDFAt Minnesota State University Mankato (MNSU), we’ve undertaken several experiments and initiatives focused on Generative Artificial Intelligence. At the start of the fall semester, we collaborated with university Information Technology Services to present a professional development session for returning faculty through the MNSU Center for Excellence in Teaching & Learning on “5 Tips for Teaching with AI.” We also presented to librarians across the regional consortium, Minitex, on “The Library & Generative AI.” This presentation included several demonstrations. It was offered as an introduction to Generative AI focused on topics most relevant to librarians, including information literacy, as well as copyright and license-related concerns. Later in the fall, we offered a daylong “Experience Friday” workshop for area high school students on “AI at the MNSU Library.” The workshop included sections on basic prompting, responsible use, learning with AI, the future of work, and the dark side of AI. Throughout the fall, we experimented with using ChatBots to help with licensing as described in the NASIG Fall conference presentation, “AI as a License Review Assistant.” Finally, leading into the spring semester, we also experimented with using Dall-E to develop collections data illustrations as described in the SUNYLA conference presentation, “Novelty Visualizations of Collections Data: Real Impact or Comic Interlude?” At the GAIL virtual conference, we reviewed these initiatives and others to consider what has worked best and what we might not repeat
Preparing Students in Speech-Language Pathology Through a Diversity, Equity, and Inclusion Lens
Get PDFSTATEMENT OF PROBLEM: As the racial diversity of the United States population increases, diversity in the field of speech-language pathology remains homogenous. Efforts to increase diversity, equity, and inclusion (DEI) continue clinically and academically. Considerable action must be taken to create an equitable space for racially and ethnically diverse students and improve interactions with racially and ethnically diverse clients. PROCEDURE: The purpose of this study was to examine students’ perspectives of clinical instruction and coursework to prepare them to practice culturally responsivity in speech-language pathology. Also, this study aimed to explore students’ experiences of learning culturally responsive care and feelings about programmatic changes. FINDINGS: Quantitative results found that Year 1 students (received programmatic changes related to DEI) felt more prepared to practice cultural responsivity than Year 2 students (did not receive programmatic changes related to DEI). Three themes emerged from the qualitative data analysis: student knowledge and skill development, student feelings about programmatic changes, and program and instructor journey. While many students noted they had limited experience with racially and ethnically diverse clients, they felt their coursework infused with DEI helped them become better prepared for the futur
Leveraging Machine Learning & Deep Learning Methodologies to Detect Deepfakes
Get PDFThe rapid evolution of deep learning (DL) and machine learning (ML) techniques has facilitated the rise of highly convincing synthetic media, commonly referred to as deepfakes. These manipulative media artifacts, generated through advanced artificial intelligence algorithms, pose significant challenges in distinguishing them from authentic content. Given their potential to be disseminated widely across various online platforms, the imperative for robust detection methodologies becomes apparent. Accordingly, this study explores the efficacy of existing ML/DL-based approaches and aims to compare which type of methodology performs better in identifying deepfake content. In response to the escalating threat posed by deepfakes, previous research efforts have focused on inventing detection models leveraging CNN architectures. However, despite promising results, many of these models exhibit limitations in reproducibility and practicality when confronted with real-world scenarios. To address these challenges, this study endeavors to develop a more generalized detection framework capable of discerning deepfake content across diverse datasets. By training simple yet effective ML and DL models on a curated Wilddeepfake dataset, this research assesses the viability of detecting authentic media from deepfake counterparts. Through comparative analysis and evaluation of model performance, this study aims to contribute to the advancement of reliable deepfake detection methodologies. The models used in this study have shown significant accuracies in classifying deepfake media