Information Studies

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Author: search.filters.author.Cleveland, Keaunna

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    Data everyday as community-driven science: Athletes' critical data literacy practices in collegiate sports contexts
    (Wiley, 2022-12-27) Clegg, Tamara L.; Cleveland, Keaunna; Weight, Erianne; Greene, Daniel; Elmqvist, Niklas
    In this article, we investigate the community-driven science happening organically in elite athletics as a means of engaging a community of learners—collegiate athletes, many of whom come from underrepresented groups—in STEM. We aim to recognize the data literacy practices inherent in sports play and to explore the potential of critical data literacy practices for enabling athletes to leverage data science as a means of addressing systemic racial, equity, and justice issues inherent in sports institutions. We leverage research on critical data literacies as a lens to present case studies of three athletes at an NCAA Division 1 university spanning three different sports. We focus on athletes' experiences as they engage in critical data literacy practices and the ways they welcome, adapt, resist, and critique such engagements. Our findings indicate ways in which athletes (1) readily accept data practices espoused by their coaches and sport, (2) critique and intentionally disengage from such practices, and (3) develop their own new data productions. In order to support community-driven science, our findings point to the critical role of athletics' organizations in promoting athletes' access to, as well as engagement and agency with data practices on their teams.
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    Creepy or Cool? An Exploration of Non-Malicious Deepfakes Through Analysis of Two Case Studies
    (2022) Cleveland, Keaunna; Shilton, Katie; Library & Information Services; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Several studies have examined the harms associated with the development of deepfake technology and its use by malicious actors, but less research has been devoted to deepfakes created by non-malicious creators and the ways people react to deepfakes developed without malicious intent. This study attempts to close this research gap through the exploration of two case studies that demonstrate non-malicious deepfake use on Instagram and Twitter. Using sensemaking, privacy as contextual integrity, and audience theory to guide the analysis of publicly available posts, tweets, and records, this study examines how people interact with and react to non-malicious deepfakes online. Building on these findings, this thesis suggests how social media platforms might integrate signifiers in their design that afford sensemaking for those interacting with deepfake technology and discusses how ethical frameworks and practices from values-oriented design and value-based engineering in design may help guide creators as they develop deepfake technology videos and applications for non-malicious purposes.