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        "event_title":"Museums at Two presentation",
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        "combined_title":"Museums at Two presentation: Women in museum computing: A brief history of data entry & skilled labor",
        "event_subtitle":"Women in museum computing: A brief history of data entry & skilled labor",
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        "description":"Presentation by Alexandria Rayburn (Museum Studies Program alumni; PhD, Information)\r\n\r\nMuseum collections are many things, but primarily they function as repositories of information, where data-intensive work has been conducted for decades. Like other knowledge infrastructures, the information in museum collections is built on installed bases and require highly skilled technicians to maintain these long-lasting digital systems. However, in representations of this computing work, women's voices and perspectives are under-appreciated, even though they have significant labor contributions in this area. \r\n\r\nMy dissertation examines the experiences of women working in computing roles within museum collections and seeks to understand their unique approaches to data management. Drawing from this research, this talk will explore some of the early gendered data practices at both the Smithsonian Institution and the Metropolitan Museum of Art\u2014 two highly regarded institutions whose leadership in early museum computing has had lasting implications on museum best practices, and the value given to computing tasks like data entry.",
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        "tags":["Information and Technology","Museum"],
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        "event_title":"In Search of Wellness: Social media influencers and the transformation of an industry",
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        "combined_title":"In Search of Wellness: Social media influencers and the transformation of an industry: Professor Mariah Wellman",
        "event_subtitle":"Professor Mariah Wellman",
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        "description":"For more than a decade, social media influencers have shared their lives with captive audiences hungry to consume entertaining, aspirational, and educational content from popular users deemed to be \u201cjust like us.\u201d What started as a hobby for many, has turned into a career for those most adept at navigating technological advances alongside follower expectations. As the influencer industry professionalized in the United States, niche creators emerged and have become central figures in the lives of individuals looking to restore their health and enhance their well-being. In her talk, Dr. Mariah L Wellman draws on her current book project to describe the phenomenon of wellness influencers and how they\u2019ve risen to prominence. She details the role of authenticity and expertise in establishing authority within the digital wellness space and the strategies influencers use to spread health information. Finally, Dr. Wellman discusses the increase in online health misinformation and the rise in institutional experts flocking to social media to debunk influencer-created content.",
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        "event_title":"Regression Discontinuity Design with Distribution-Valued Outcomes",
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        "combined_title":"Regression Discontinuity Design with Distribution-Valued Outcomes: David van Dijcke, University of Michigan",
        "event_subtitle":"David van Dijcke, University of Michigan",
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        "description":"This article introduces a regression discontinuity design for distribution-valued outcomes (R3D), extending the standard RDD framework to settings where outcomes are entire distributions rather than single values. This arises when treatment is assigned at the group level (e.g., firms, schools) but the objects of interest are within-group distributions (e.g., employee wages, student test scores). Standard RDDs are not designed for this two-level structure, as they assume scalar outcomes observed at the same level as treatment assignment. To address this, I develop a novel approach based on random distributions and show that, under a mild continuity condition on the average quantile function, the jump at the cutoff identifies a local average quantile treatment effect. To estimate it, I propose a distribution-valued local polynomial estimator, which fits the full quantile curve with a single bandwidth, avoids quantile crossing, and yields a meaningful \u201caverage distribution\u201d. I derive uniform asymptotic normality, valid multiplier bootstrap confidence bands, and a data-driven bandwidth selection method. Simulations demonstrate strong performance and reveal that standard quantile RDD is biased and inconsistent in this setting. An application to U.S. gubernatorial close elections (1984\u20132010) uncovers an equality\u2013efficiency trade-off under Democratic control, driven by income reductions at the top of the distribution.",
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