bluuebunny/crossref_metadata_2025_split huggingface dataset:
Conceptions of Everyday Life in Information Science
Authors: Hartel Jenna , Ndumu Ana , Ocepek Melissa , Ruthven Ian 
Published: October 2024

ABSTRACTThis panel examines conceptions of everyday life in Information Science. Several theories about everyday life and its information phenomena will be reviewed and analyzed for their origins, distinctions, and divergent claims. Four expert panelists who have published on these matters will encapsulate their ideas, and there will be a video interlude, as well. By design, the panel Agenda features short opening statements, leaving 40 minutes to discuss: How do existing notions of everyday life within Information Science bring information into focus in different ways? Are informational conceptions of everyday life adequate or wanting of critical re‐examination? In keeping with ASIS&T's multiperspective community, inputs will be sought from students, practitioners, first‐time conference attendees, and other groups, in turn. If, as Marcia J. Bates claims, we are “…always looking for the red thread of information in the social texture of people's lives” (1999, p. 1048) then we need to individually and collectively reflect on the nature of everyday life.
AI for Library and Information Science (AI4LIS): Towards a Research Agenda
Authors: Hofman Darra , Novin Alamir , Ghosh Souvick , Mohammadi Ehsan 
Published: October 2024

ABSTRACTThis 90‐minute panel is structured to engage the ASIS&T community in discussion of the methods which LIS researchers and educators are using to interrogate, develop, understand and teach about and with artificial intelligence. In particular, this panel will invite the audience to join in an open fishbowl to surface how people in LIS are engaging with artificial intelligence, to identify the most pressing gaps and needs, and to lay the initial groundwork for an AI research agenda for LIS.
Exploring the Application of Artificial Intelligence and Machine Learning in GLAM Collections
Authors: Kim Jeonghyun , Chen Haihua , Yang Le , Simic Julia 
Published: October 2024

ABSTRACTIn recent years, considerable work has been done on the application of artificial intelligence (AI) and machine learning (ML) in the realm of cultural heritage. The purpose of this panel is to address the following questions: What is the current understanding and implementation status of AI/ML in GLAM collections? What are the associated concerns and challenges in real cases? What does this entail in applying AI/ML in the field of computational archives? To address these questions, the panelists will present: 1) use cases of AI/ML technologies applied within GLAM collections, 2) findings from a systematic review of literature on AI/ML in GLAM collections, and 3) insights from semi‐structured interviews with archival practitioners on their perspectives on AI/ML. Following the panelists' presentations, an interactive discussion session will be conducted to delve deeper into the topics discussed.
Social Risks in the Era of Generative AI
Authors: Liu Xiaozhong , Lin Yu‐Ru , Jiang Zhuoren , Wu Qunfang 
Published: October 2024

ABSTRACTGenerative AI (GAI) technologies have demonstrated human‐level performance on a vast spectrum of tasks. However, recent studies have also delved into the potential threats and vulnerabilities posed by GAI, particularly as they become increasingly prevalent in sensitive domains such as elections and education. Their use in politics raises concerns about manipulation and misinformation. Further exploration is imperative to comprehend the social risks associated with GAI across diverse societal contexts. In this panel, we aim to dissect the impact and risks posed by GAI on our social fabric, examining both technological and societal perspectives. Additionally, we will present our latest investigations, including the manipulation of ideologies using large language models (LLMs), the potential risk of AI self‐consciousness, the application of Explainable AI (XAI) to identify patterns of misinformation and mitigate their dissemination, as well as the influence of GAI on the quality of public discourse. These insights will serve as catalysts for stimulating discussions among the audience on this crucial subject matter, and contribute to fostering a deeper understanding of the importance of responsible development and deployment of GAI technologies.
The People behind the Research: How Three Researchers Utilized Their Diverse Positionality to Study LGBTQ+ Archives
Authors: Oltmann Shannon M. , Wagner Travis , Martell Allan 
Published: October 2024

ABSTRACTIn this panel, three researchers with distinct positionalities will discuss how their unique perspectives contributed to the overall success of one research project (though with some inherent challenges). First, we review our respective positions with regards to the research project, a study of LGBTQ+ archives. Then we describe how our subjective pasts and experiences interacted as we conducted the research. With this panel, we intend to open a conversation about how one's individual background and perspective can both conflict and harmonize with others, leading to interesting differences in research approaches, methods, and analysis. This panel will explicitly include time for the audience to reflect on their own positionality with respect to their research; we anticipate a richly nuanced discussion.
Provocations on iSchools and Librarianship: New Priorities for LIS Forward
Authors: Palmer Carole L. , Renear Allen , Dempsey Lorcan , Hall Tracie D. , Marchionini Gary , Subramaniam Mega , Coward Chris 
Published: October 2024

ABSTRACTFor the last twenty years, the field of library and information science (LIS) has been evolving within the context of iSchools. Now, organizationally melded with other information fields, LIS is better positioned than ever to synergize expertise across disciplines to benefit librarianship. However, the recent LIS Forward position paper asserts that dynamics are changing within iSchools in ways that can strengthen or diminish LIS, surfacing critical tensions and issuing recommendations for the iSchool community. This panel assembles thought leaders to share new perspectives on the provocations of the position paper and to further advance the conversation with the ASIS&T community. The group will consider themes of diversification, competition, and identity and explore two urgent professional concerns—Black librarianship and AI. A moderated discussion will be documented to inform future engagement on charting directions, investments, and grand challenges for the field of LIS and the profession.
Exploring Some Impacts of Advances in Artificial Intelligence: A Social Informatics Approach
Authors: Rosenbaum Howard , Gumusel Ece , Sanfilippo Madelyn Rose , Sweeney Miriam , Sawyer Steve , Zhou Kyrie Zhixuan 
Published: October 2024

ABSTRACTThis panel explores the evolving relationship between technology and society through the lens of Social Informatics (SI) research, with a specific focus on the integration of Artificial Intelligence (AI) into social and organizational settings. Experts will discuss how AI adoption across various sectors influences social dynamics, cultural norms, and ethical considerations for a range of stakeholders. Key themes include responsible AI development, user privacy concerns, and addressing biases in AI algorithms. Through interdisciplinary insights, the panel aims to engage the audience in a lively discussion of AI's impact on society, exploring current challenges and opportunities at the intersection of SI and AI research.
Human Subjectivity in Information Practice and AI Governance
Authors: Wang Fang , Zhang Chao , Yang Shengnan , Liu Xiaozhong , Liu Ying‐Hsang 
Published: October 2024

ABSTRACTThe rise of Artificial Intelligence (AI) introduces a notable tension in the realm of traditional, human‐centric information practices, where human subjectivity has been pivotal in both influencing and being influenced by our interactions with information. An excessive reliance on AI distances humans from practices, potentially diminishing human subjectivity. Additionally, as AI takes on roles once exclusively human, it might constrict opportunities for personal growth and the cultivation of unique insights. Moreover, this technological dependency could dilute the richness of direct human interactions, weakening the fabric of social bonds. These issues—increased AI dependence, AI's encroachment on human roles, and the degradation of social ties—underscore the urgent necessity to revisit our interaction with technology, ensuring it serves to enrich rather than undermine the human experience. In light of this, our panel gathers experts to explore strategies for preserving human subjectivity through cognitive autonomy, creative agency, and social connectivity in the age of AI‐driven information practices. Our dialogue also aims to develop a comprehensive AI governance framework, scrutinized from an interdisciplinary perspective, continually refined in collaboration with academic communities, such as ASIS&T, to solidify and enhance our approaches.
Siphonophorae, an Intervention on (In)Visible Infrastructure
Authors: Bettivia Rhiannon , Stallworth Rebecca , Maurici‐Pollock Danielle , Williams Rachel 
Published: October 2024

ABSTRACTThis alternative event uses siphonophorae as a metaphor for examining infrastructure in information work. Infrastructure studies are deeply rooted in the field of information science: Susan Leigh Star's work forms a cornerstone of infrastructure studies and the ways in which information science views how people operate within the systems around them (Star, 1999; Larkin, 2013). Representing a broad meta‐discipline, the ASIS&T community has decades of research and publications on infrastructure, and because the topic is so complicated and rhizomatic, we still lack clear perspectives on key questions like: Why this infrastructure, and why now? Attendees will work together to construct a physical and a digital siphonophore that constitute connections between infrastructure; those who design them; those who use them; and how they function in relation to information sciences and spaces. We seek to render infrastructure visible by asking: why this infrastructure? Doing so enables us to further interrogate: what infrastructure could be better?
Developing a Text‐Based Measure of Humility in Inquiry Using Computational Grounded Theory
Authors: Bratt Sarah , Leahey Erin , Gomez Charles , Lee Jina , Kwon Yeaeun , Lassiter Charles 
Published: October 2024

ABSTRACTWe describe a project in which we develop a text‐based measure of HI in the context of scholarly communication using corpora of scientific publications. The data and analytic approach we use will circumvent known concerns with self‐reported data on humility levels and will be calculable on a large scale. We use a computational grounded theory approach to develop a text‐based measure of HI. We draw from an annotated corpus of scientific articles in economics, psychology, and sociology (2010–2023), generating three supra‐dimensions of HI (Epistemic, Rhetorical, and Transparent) and several novel sub‐codes of HI. We present our initial analysis with a focus on the three dimensions of HI derived from a computational grounded theory approach. The text‐based measure helps us better understand how contextual factors shape HI and contribute to mixed methods in information science research.
Parasitic Platforms and the Crisis in News
Authors: Brighton Jack 
Published: October 2024

ABSTRACTThe viability of news has been in rapid decline since the mid‐2000s. This poster presents a critical analysis of how news publishers themselves helped precipitate the crisis by enthusiastically adopting Big Tech platform technologies and audience‐building strategies. I show how search and social media platforms disrupted publishers' relationships with audiences and advertisers by appropriating control over news distribution and revenue. I use Anthony Giddens' structuration theory and Bruno Latour's actor‐network theory to explore the restructuring of the news industry by the sociotechnical practices and surveillance economics of today's dominating platforms. Leveraging Michel Serres' concept of social parasitism, I present a research framework for assessing symbiotic and parasitic relationships in sociotechnical systems using historical, quantitative, and qualitative methods, and to identify where news publishers still have agency to begin resolving the crisis. And I suggest the urgency of this research framework as publishers rapidly adopt new AI technologies.
Doctoral Student Perspectives of Support for Seeking Mental Healthcare On‐Campus: Preliminary Findings
Authors: Carucci Damon 
Published: October 2024

ABSTRACTEven as the number of doctoral students reporting mental health challenges continues to grow, many students who experience significant mental distress do not seek treatment for these concerns. To explore this phenomenon, thirty semi‐structured interviews were conducted with doctoral students at a large U.S. public university. This poster reports on a candidate theme from a larger qualitative analysis that explored the barriers doctoral students face when seeking mental healthcare on‐campus. Insights from the theme “minimal institutional support” are presented here, and discussed with respect to health literacy, information marginalization, and consumer health information justice.
Investigating the Applications of AI in Oncology from NIH Funded Projects: Case study of Lung Cancer and Pancreatic Cancer
Authors: Cherukuri Komala Subramanyam , Chen Haihua 
Published: October 2024

ABSTRACTThis poster aims to conduct an examination of the applications of Artificial Intelligence (AI) in oncology. We collect projects from National Institutes of Health (NIH) which delve into how AI applications and techniques are used in cancer research between 2018 to 2024. Results show AI's ability to enhance lung and pancreatic cancer through novel imaging techniques and data processing. We find that refined medical imaging can be done with AI‐driven techniques such as machine learning or deep learning to improve diagnostic accuracy potentially leading to better survival rates for patients involved. Additionally, this reveals how AI transformed oncology studies underlining the movement toward complex diagnostic algorithms that allow accurate stage assignments. Consequently, these forms of teamwork are essential when determining as well as making reliable Cancer care systems by developing strong AI systems with interpretation abilities using supportive NIH funding.
(Disrupting) Continuities between Eugenics and Statistics: A Critical Study of Regression Analysis
Authors: Dodson Samuel , Bartley Jane 
Published: October 2024

ABSTRACTThis paper critically examines the intertwined history of statistics and eugenics through the work of Francis Galton, whose statistical inventions were guided by his problematic belief in eugenics. The paper highlights the historical development of regression analysis, arguing that acknowledging the discriminatory origins of this method is crucial for understanding historical and contemporary injustices in data‐driven decision‐making. The paper also considers the ethical implications of other statistical techniques, emphasizing the need for library and information science (LIS) students and practitioners to be aware of the societal implications of “objective” data analysis methods.
The Human‐Data Interaction Driven by Data Reuse
Authors: Duan Qingyu , Liang Mengli , Wang Xiaoguang 
Published: October 2024

ABSTRACTThe data‐intensive research paradigm is sweeping through the scientific community, and new interaction challenges for interacting with data have emerged. Data reuse emerges as a critical driver of human‐data interaction, positioning data repositories at the forefront as the optimal solution for facilitating this process. This study adopts a diary study methodology to analyze the data repository‐supported human‐data interaction behaviors driven. The findings reveal that human‐data interaction extends beyond mere engagement with data, encapsulating elements of human‐computer interaction, engagement with literature, interactions with agent systems, and interpersonal communication.
Community Partnerships in Digital Cultural Heritage: A Systematic Review
Authors: Fenlon Katrina , Wise Nikki M. , Sorensen Amanda H. 
Published: October 2024

ABSTRACTCommunity partnerships with libraries, archives, and museums are increasingly recognized as essential to developing and stewarding complete, accurate, and equitable digital cultural records. While research and practice in this area has burgeoned, there is limited empirical research characterizing the state of the field. This study offers preliminary findings of a systematic, state‐of‐the‐art review of community‐institutional partnerships in digital cultural heritage collections and initiatives. This review systematically identifies wide‐ranging, active community partnerships among relevant institutions and their high‐level objectives, and a correlated array of approaches taken in pursuit of those objectives. The resulting conceptual framework and open access dataset form the base of a toolkit supporting community‐institutional partnerships for a more sustainable, community‐centered cultural record.
Describing Qualitative Research in ASIS&T Publications, 2018–2022
Authors: Frank Rebecca D. , Kriesberg Adam , St. Hilaire Monique 
Published: October 2024

ABSTRACTThe Information Science/LIS research community welcomes research across the methodological spectrum, including qualitative methods featuring human subject research. This poster presents the research design and data collection process for an exploratory study examining qualitative research published as both long and short papers in ASIS&T Annual Meeting proceedings from 2018–2022.
Who Am I? How Public Library Staff Manage Conflicting Information about the Self‐Concept
Authors: Freeburg Darin , Klein Katie 
Published: October 2024

ABSTRACTThis poster describes how public library staff manage conflicting information about their identity. Public library staff completed audio diaries about times throughout the workday that made them feel frustrated or uncomfortable, revealing times when external and internal information about their self‐concept conflicted. Their responses were coded according to established conflict styles, revealing a dynamic approach to identity construction.
Unlocking Scholarly Realms: Revealing Discipline‐Specific Publication and Citation Benefits in Open Access
Authors: Hadad Shlomit , Aharony Noa , Raban Daphne R. 
Published: October 2024

ABSTRACTThis bibliometric analysis examines research publications from Israel, Austria, and Mexico (2010–2020) using Scopus data. The selection of Austria and Mexico as comparators to Israel was based on SCImago rankings. Top four disciplines were chosen for Health, Physical, and Life Sciences (HPL) and Social and Humanities Sciences (SSH). The findings indicate a distinct preference for closed‐access publications; however, a clear ‘open access citation advantage’ emerges nonetheless. Notably, Israel has the lowest SSH publications in open access, yet the highest in closed access. Conversely, open‐access HPL publications receive more citations than closed access across all countries. Despite fewer open‐access articles, Israel's HPL research surpasses Austria in citations. In SSH, Israel, and Austria's open‐access articles attract more citations, while Mexico shows no significant difference. These findings provide insights into publication dynamics, disciplinary influences, and citation patterns.
The Influence of AI Literacy on User's Trust in AI in Practical Scenarios: A Digital Divide Pilot Study
Authors: Huang Kuo‐Ting , Ball Christopher 
Published: October 2024

ABSTRACTThis study explores the impact of Artificial Intelligence (AI) literacy on trust in AI across critical sectors, including transportation, healthcare, and social relationships. An online survey of 300 participants was conducted to examine trust levels in six practical AI application scenarios. The findings revealed that individuals with advanced AI literacy consistently demonstrate higher trust across all scenarios. In contrast, those with intermediate AI literacy exhibit more skepticism, particularly in high‐stakes contexts such as transportation and healthcare. This result indicates that the disparities in AI literacy can significantly shape trust levels, and the context of AI use matters. Therefore, targeted educational programs are needed to improve AI literacy, rectify misconceptions, and promote broader acceptance and trust in AI technologies. Further research should expand the demographic scope to further validate these findings and optimize educational initiatives for inclusive and equitable AI integration.
Public Perceptions of GAI: A YouTube Analysis of Sora
Authors: Huang Shilan , Goh Dion Hoe‐Lian 
Published: October 2024

ABSTRACTText‐to‐video generative AI (GAI) tools are captivating the world with hyper‐realistic content. However, public perceptions of such technologies remain ambiguous. This paper investigates Sora, a new OpenAI offering, as an example to understand the public perceptions of GAI technology. The data comprises the 39 most popular YouTube videos about Sora and their most‐liked user comments. Through a SWOT analysis, we identify the multifaceted portrayals of Sora in the videos and the corresponding comments. Implications for future research are discussed.
Perceived Online Information Quality: An Empirical Study Using Q Methodology
Authors: Ju Boryung , VanScoy Amy 
Published: October 2024

ABSTRACTThe aim of the current study is to identify the dimensions of perceived online information quality and apply Q methodology to analyze users' perceptions both qualitatively and quantitatively. Data were collected from readers and contributors of English Wikipedia through two rounds of surveys. The analysis of the first round, with 197 participants, identified 36 dimensions of online information quality, while the second round with 68 participants yielded significant five factor categories of information quality with Q methodology. Q methodology was employed to configure the design and analysis of the second round of research, especially aiming to transform users' opinions and perceptions into more solid evidence.
Tech Treasure Hunt: Promoting Children's Learning on How Algorithm Works at a Public Library
Authors: Jung Yong Ju , Liu Jiqun , Karahan Harun , Nazari Mahdieh 
Published: October 2024

ABSTRACTPeople in this era are surrounded by data, but still, a lot of people do not fully understand what processes occur behind the scenes such as search and recommendation algorithms trained on data of varying forms. Even though not all children will become data scientists alike, enhancing such awareness and data literacy is essential for their critical thinking and becoming active agents in the 21st century. In this regard, as part of a design‐based project, this paper demonstrates our intervention—a summer program at a public library—where children and families learned about algorithms in playful ways and shows how they made sense with the concept of algorithms.
From Fandom to Fadeaway: Unpacking Factors Contributing to Anime Dropout
Authors: Lee Wan‐Chen , Hirt Juliana , Cho Hyerim 
Published: October 2024

ABSTRACTThis study explores a case of anime dropout/disengagement to understand media information users. Particularly, this study identifies elements in anime (e.g., Tropes) that prevent audience from continue watching what they had started (i.e., dropout/disengagement). A content analysis of 226 posts from MyAnimeList.net, an online anime database and fan community, suggests that the major elements that motivate audience dropout are Plot/Narrative, Tropes, Artwork/Visual Style, Characters, and Mood. The expectation gap and sunk cost also contribute to disengagement. This study presents a way to enrich anime metadata through analyzing the identified elements. Additionally, this study suggests that understanding anime disengagement may contribute to the search and recommendation of anime.