Conference Agenda (All times are shown in Mountain Daylight Time (MDT) unless otherwise noted)

The 24th Annual SIG-USE Research Symposium focuses on how information behavior researchers and professionals can endeavor to center people and real-world outcomes in their work. As we continue to build and leverage advanced technologies, such as AI, to generate new insights and improve workflows, being mindful of how our work can impact individuals, particularly the ones that are often overlooked during the development and usage of information systems and technologies, is paramount. This symposium provides an opportunity for researchers, scholars, students, faculty, and information professionals to discuss ways in which the values of responsibility, reciprocity, and care can be incorporated into our work through effortful and informed design and practice. Participants will engage in panel discussions and breakout sessions to think of ways they can apply “people-first” approaches and mindsets into their everyday work. This symposium is open to all ASIS&T and non-ASIS&T members. Additional registration fee applies.

Women who work in library IT professions often struggle with being seen, heard, valued, and respected. Research has shown that early experiences of belonging or lack thereof determine how women confidently navigate the tech space or silently struggle with daily tasks, project management, collaboratively participate in groups, and/or lead in a male dominated space. Communication styles, bias, and work-life balance all contribute to differences in navigating and succeeding in this environment. This workshop aims to create space for open and direct discussion about this often uncomfortable topic of gender bias in teams. We will explore topics of identifying personal values and contributions, learning to use our “voices” effectively, the power of reciprocity, and identifying support systems. The workshop will include self-awareness and interactive activities, movement facilitation, reflection time, and strategic directions forward.

Participants will learn the following: Data and statistics about women in information technology fields. The importance of belonging on teams. Gain self-awareness insights and how to use strengths in the workplace. How to strategize when working with male colleagues and how to look for allies. Additional registration fee applies.

In the rapidly evolving landscape of information science and technology, the intersections of social media algorithms, Artificial Intelligence (AI), large language models (LLM), AI-Generated Content (AIGC), and Social Informatics represent emerging areas of research, development, and societal impact. This workshop, aims to bring together leading researchers, industry practitioners, policymakers, and community leaders in the field of Social Infomatics (SI) to discuss recent advances, challenges, and future directions in these interrelated domains. Through a combination of short and long paper presentation and discussion session, participants will explore the technical, ethical, and social implications of AI and AIGC, with a special focus on their roles in shaping the fabric of social informatics. ASIS&T SIG-SI has hosted 19 annual Social Informatics Research Symposia during the ASIS&T Annual Meeting for the purpose of disseminating and discussing current research and research in progress that investigates the social aspects of information and communication technologies (ICTs).To emphasize the meeting theme this year, we aim to center around the interactions of SI, everyday life, and the algorithmic environment. Contributors but not presenting: Pu Yan, Zaheer Ahmad, Ruwan Gamage, Alicia JW Takaoka. Additional registration fee applies.

This interactive workshop will unpack the Institute of Museum and Library Services (IMLS) application and peer-review process. Following a brief introduction to its grant programs and initiatives, IMLS staff will hold a discussion with four recent peer reviewers who have also been awarded IMLS grants themselves. They will speak about their experience reviewing IMLS grant proposals as well as applying for and conducting their funded projects. Workshop attendees and presenters will join the discussion and then break out into small group activities. These personalized informal small group discussions will provide space for individuals to consider or revise an IMLS grant application and to ask questions in the moment, with the aim of reducing barriers to application. Organizers are especially excited to welcome early career investigators, prospective, and first-time applicants. Attendees are encouraged to bring draft grant applications and proposal narratives for discussion. This workshop will cover these learning objectives: How to choose an appropriate IMLS grant opportunity; How to consider your career path when shaping your applied research proposal; Understanding the peer-review process; Putting together and submitting a competitive grant proposal; Implementing a successful grant.

Additional registration fee applies. IMLS will pay for the first 25 registrants on a first-come, first-served basis.

This half-day workshop aims to enhance the Library and Information Science (LIS) community's engagement with Large Language Models (LLMs) such as ChatGPT, LLaMA, BERT, and other advanced artificial intelligence (AI) technologies. We seek to foster collaboration among researchers, practitioners, educators and students for the impactful integration of LLMs within LIS fields. The workshop will enable attendees to share their experiences evaluating and applying LLMs in various professional activities and highlight challenges and opportunities from the LIS perspectives. Through a mix of a keynote talk, four paper presentations, a series of lightning talks, and two rounds of collaborative discussions, this workshop aims to contribute to the development of evidence-based strategies for integrating LLMs into LIS research, practice, and education. This workshop is sponsored by SIG AI. Additional registration fee applies.

This half-day workshop aims to promote discussion and disciplinary convergence on the topic of social media research, focusing on issues related to mis/disinformation, online communities, user behavior and engagement, digital activism, and emerging social media technologies and platforms. Social media has become a mainstream channel where users create, consume, and exchange information. It has also emerged as a burgeoning field of research interest, attracting researchers from various disciplines. The ASIS&T community is positioned at the heart of this domain, including researchers with diverse backgrounds and expertise who share similar interests in theories, techniques, and technologies to improve information access and communication. This workshop aims to: 1) highlight current social media research opportunities and challenges, 2) discuss the threats and opportunities with emerging AI technologies like ChatGPT, 3) identify the trending and important social media research, and 4) guide new researchers in their study and research development. The workshop would be beneficial for the ASIS&T community by bringing scholars and practitioners from diverse disciplines, facilitating interdisciplinary collaborations, and fostering new research agendas. Additional registration fee applies.

The goal of this workshop is to bring together scholars who are confronting the question of what it means to be an information scientist and educator in the zeitgeist of generative AI and to explore and examine the (potential) futures of information science education. It will be highly interactive, involving all the participants in active forms of brainstorming and collaboration to imagine and conceptualize possible futures and to grapple with how faculty instructors ought to respond to, or even build, these futures to improve information science education in the years to come. The workshop format will be based on an interactive Futures Workshop model (Kensing and Madsen, 1991; Lauttamäki, 2014). This workshop model is specifically useful “when seeking answers to practical questions (e.g. future of an industry) and devising action plans for achieving desired future at a one-day workshop” (Lauttamäki, 2014, p. 2). Through this interactive format, the workshop seeks to build a network of information science scholars confronting issues around AI and education at their own institutions, and to provide a space for researchers who have early projects on understanding AI’s impact on teaching and research to share their work and findings. Additional registration fee applies.

Methods for eliciting reasoning from large language models (LLMs) are shifting from filtering natural language “prompts” through contextualized “personas,” towards structuring conversations between LLM instances, or “agents.” This work expands upon LLM multiagent debate by inserting human opinion into the loop of generated conversation. To simulate complex reasoning, LLM instances were given United States district court decisions and asked to debate whether to "affirm" or "not affirm" the decision. Agents were examined in three phases: “synthetic debate,” where one LLM instance simulated a three-agent discussion; “multiagent debate,” where three LLM instances discussed among themselves; and “human-AI debate,” where multiagent debate was interrupted by human opinion. During each phase, a nine-step debate was simulated one-hundred times, yielding 2,700 total debate steps. Resulting conversations generated by synthetic debate followed a pre-set cadence, proving them ineffective at simulating individual agents and confirming that mechanism engineering is critical for multiagent debate. Furthermore, the reasoning process backing multiagent decision-making was strikingly similar to human decision-making. Finally, it is discovered that while LLMs do weigh human input more heavily than AI opinion, it is only by a small threshold. Ultimately, this work asserts that careful, human-in-the-loop framework is critical for designing value-aware, agentic AI agents.

This study evaluated the performance of ChatGPT-3.5 and ChatGPT-4 in developing search strategies for systematic reviews. Using the Peer Review of Electronic Search Strategies (PRESS) framework, we employed a two-round testing format for each version. In the first round, both versions displayed comparable competencies when assessed quantitatively by the PRESS measures. However, qualitative feedback from two professional health sciences librarians indicated that ChatGPT-4 outperformed ChatGPT-3.5, particularly in suggesting MeSH term inclusion and refining search strategy formulations. In the second round, prompts were refined based on the feedback from the previous round of testing. Both qualitative and quantitative evaluation results confirmed ChatGPT-4’s superiority. This study provides empirical evidence of advancements in language model capabilities, highlighting ChatGPT-4’s enhanced efficiency and accuracy in developing search strategies for systematic reviews.

This paper reviews the concepts related to the digital reproduction of and access to cultural heritage items, often referred to as Digital Repatriation, Digital Restitution, or Digital Return. Using a scoping review and thematic analysis, we reviewed over 30 years of literature on this subject and review key themes that are important for students and scholars interested in cultural heritage informatics: the impact of historical and ongoing colonialism and imperialism; Access to cultural heritage through digital surrogacy; The return of power, control and authority to originating communities; and the importance of collaboration and partnership. This scoping review also documents this shift from the first uses of the term digital repatriation to the more critical concepts used today.

This paper explores the conceptual relationship between archives and canonization by amending Aleida Assmann’s cultural memory framework of Canon and Archive to consider ongoing debates in archival theory and practice. The process of canonization concerns the selection, value, and duration of cultural memory. The formation and function of archives are marked by processes of appraisal (record evaluation and selection), acquisition (record transfer), and preservation (record duration). Placing A. Assmann’s framework in conversation with the informational frameworks of de Fremery and Buckland, we observe how at any given time (a past/present context), records (documents) are selected (perception) because of their value in signaling memories and community histories (affordances) while serving as a durable medium for restabilizing identities (use) across time (a present/future context).

The CARE Principles for Indigenous Data Governance provide essential guideposts for the stewardship of Indigenous data. To put CARE into practice in libraries and repositories, resources are needed to support implementation and integration into current research data services (RDS). This paper builds on case studies with scholars of Indigenous language and culture, articulating specific Indigenous research and data practices to help guide metadata work and other areas of responsibility in RDS. The cases surface the richness of relationships and the significance of accountability in the research process—demonstrating the “relational accountability” inherent in Indigenous research methods. Robust representation of relationality is essential to retaining integrity of context in metadata for Indigenous research data. We consider the practical implications of documenting relational context with current descriptive metadata approaches and challenges toward achieving CARE adherent metadata, which we argue is the backbone for broader application of CARE for Indigenous RDS.

This research proposes an ethical framework for engagement with the data collected by information institutions on and about their community members, part of a field better known as community informatics. As data collection becomes a key element of library and information patron services, it is crucial that workers have experience and training in dealing with the collection and handling of data related to their patrons. The PACT framework, which stands for Power, Access, Consent, and Trust, was developed and workshopped in LIS classrooms across three ALA-accredited programs and aims to prepare LIS students for the types of ongoing relationships and issues they will encounter working in an institutional setting. The framework is grounded in critical theory, and takes a participatory approach towards community informatics, as well as drawing on the Codes of Ethics of three major professional organizations for cultural heritage and information professionals

We report on the results of a content analysis of a random sample of tweets (n = 16, 480) about the January 6 insurrection published one year after the attacks. Drawing from research on social media memory, far right activism, and social memories in times of crises, we performed a deductive manual coding of the tweets. We found that roughly three of every ten Twitter users remembered the U.S. Capitol attack as an insurrection. Importantly, though, the insurrection frame was used by users who were in favor and against the attack. The other most frequent frames were censorship, used by people who aimed to discredit the idea that there had been an insurrection; treason, used by people across the political spectrum to claim that “the other side” had betrayed the republic; fear of other, employed by users who blamed “the others” for the insurrection and its aftermath; and accountability, used by people against the insurrection to call for the attackers to face justice and its leaders to be barred from holding public office. We also noticed that the different frames were not exclusive. Those who used the term “insurrection” frequently used others such as, accountability, censorship, treason, terrorism, and conspiracy.

This study examines the information behaviour of academics and professional staff undertaking the work involved in enabling the societal impact of research (or impact work) in the context of changing organisational priorities, and increased emphasis on collaboration and innovation practices in academic research. Overall, impact work requires (and leads to) different information behaviours than for traditional academic activities (e.g., publishing, grant writing). Increasing expectations that research provides tangible and demonstrable benefits to society represents a paradigm shift that is shaping university information environments, creating new information needs, opportunities, and experiences for academics. The activities involved in achieving societal impact are complex and require information, skills, time, and resources not traditionally prioritised in universities. New roles for professional staff in enabling societal impact are emerging, as universities establish supports to develop researchers’ capabilities and meet government and funder requirements. However, limited research examines the information behaviour implications of impact work for both academic and professional staff.

In the Library of Congress Subject Headings (LCSH), the current subject heading to describe Japanese American incarceration during World War II is Japanese Americans – Forced removal and internment, 1942-1945. Both scholars and community members have protested against the usage of the word internment, and catalogers appear to support this argument by assigning contradictory subject headings alongside forced removal and internment. This paper quantitatively analyzes the database created from three academic libraries to demonstrate the ontological conflict within the classification system and demonstrates the consequences of such conflict. By applying concepts that guide the formation of LCSH—such as literary warrant— the paper contributes to the ongoing discussion regarding flaws of the system using previously understudied subject headings.

This article presents qualitative research aimed at deriving key competencies for archival students and professionals to leverage Artificial Intelligence (AI) and Machine Learning (ML) to support the ongoing availability and accessibility of trustworthy public records. Methodology and findings are presented. Our primary source of data were interviews conducted between January and July 2023 with 10 archivists, record managers, and digital archives researchers from the UK, Canada, USA, Australia, and New Zealand. All our interviewees had practical experience applying AI and ML to the processing of records in archives or record management offices. Findings from the qualitative data analysis from the interviews are presented and organized in three areas of competencies: knowledge, skills, and attitudes.

Member engagement can benefit professional associations, their members, and the profession. Rarely any studies adopt the "information practices" approach to identify the factors influencing professional associations' member engagement. The experiences, epiphanies, and the frequency of 11 information practices of six SIG-III officers and volunteers when planning and implementing 184 activities of eight initiatives from 2020 to 2023 helped this autoethnography study identify 99 sub-factors influencing the member engagement online. Information production, dissemination, recording, use, and discovery emerge as the top 5 information practices of officers and volunteers, in the same order, for influencing the SIG member engagement. Managing member attendance, Sharing knowledge, Managing member attention, Meeting member needs, and Building trust serve as the top 5 factors, in the same order, for affecting the member engagement. We propose a theoretical model and provide guidance to associations to enhance and sustain member engagement.

This paper centers on feminism in post-World War II information science, namely in the context of the American Documentation Institute (ADI) and the subsequent American Society for Information Science (ASIS). We focus on the years between 1962, when ADI elected its first woman president, and 1988, when it celebrated its 50th anniversary. We make three scholarly interventions in this research. First, we contribute to the history of information science, particularly to the history of women in information science. We bring, moreover, a feminist epistemology lens to bear on information science. This involves considering both women’s participation, representation, and marginalization, and the ways in which information scientists approach women’s lives, experiences, and bodies and by extension, what they view as legitimate scholarly questions and methods. Finally, we situate information science in the broader history of science, juxtaposing ADI/ASIS with other scientific societies’ engagement with feminism. In focusing on women’s empowerment and feminism’s investment in effecting systemic change, this paper aligns with the annual meeting theme, “Putting People First: Responsibility, Reciprocity and Care in Information Research and Practice."

The LGBTQIA+ community has come under increasing hostility in the North America through policy and challenges to library holdings and funding. These challenges to professional jurisdiction have prompted renewed scrutiny of the library-as-institution’s responsibility toward society and the LGBTQIA+ community specifically. Safe spaces for this community remains a nebulous and contested concept. This paper reports preliminary findings of conceptualizations of LGBTQIA+ safe spaces in scholarly and practitioner literature in library and information science. Undertaking a critical literature review, this work outlines conceptualizations of the librarian’s role in cultivating safe spaces and contrasts the theme of safe spaces as consisting of in-group safe spaces. This paper collocates discussions of sociocultural contexts informing the ideas within literature alongside critical appraisals of and tensions with lived experience of queer folks. Surfaced is the gap between the two bodies of knowledge and conceptual and practice-oriented approaches to reduce this gap.

Public libraries are rapidly changing in response to societal and technological shifts. This change comes with serious demands on the identities of library staff, who are asked to be all things to all people. This represents a type of identity regulation, which is a form of socio-ideological control employed by organizational actors to define worker identities. While this manufacturing of subjectivity can be unproblematic, it can also lead to alienation, exhaustion, and a loss of authenticity. In this study, 21 public library staff members in the SE United States recorded audio diaries and sat for an interview about their experiences in routine library work. Findings reveal the presence of several different identity suggestions centered around things like productivity, continuous improvement, customer service, and organizational citizenship. Participants responded to these regulation attempts in several ways, including identification with the suggested identity, dis-identification, and ambivalence. Findings suggest the need for proactive support structures that enable staff to align their work identities with their authentic selves.

An important aspect of human information behavior research is to understand the motivations of people as they interact with information and technologies to make a difference. This paper presents exploratory findings of a qualitative study of public librarians’ motivations to expand civic engagement for racial justice in the American South. The geographical region is stereotypically characterized historically as being solely racially intolerant in public perceptions, scholarly discourse, political networks, and news media. This research challenges such limited notions, highlighting public librarian’s logic-based motivations to further civic engagement for racial justice in context of the nation’s historical/contemporary racialized political climate. In the process, the authors acknowledge the voices/perspectives of public librarians, an often-underrepresented group in privileged academic information behavior research. Thematic narrative analysis is explored as a strategic mode of presenting the findings.

Immersive narrative creates a vivid experiential environment that allows users to be participants rather than mere observers, holding immense potential in the field of cultural heritage. However, current research primarily focuses on technological applications and integration, lacking in-depth exploration of the fusion between technology and content as well as limited user engagement. Following a systematic review methodology, this study conducted searches and two rounds of screening in 7 major databases, resulting in the inclusion of 176 studies on immersive narrative in cultural heritage conservation. The study summarizes and extracts the current state, development trajectory, trends, research methods, and key elements in this field, innovatively categorizing them into 6 core levels of research patterns while proposing corresponding optimization strategies. Additionally, this study identifies research gaps and suggests future directions, aiming to facilitate the enhancement of immersive narrative development and deepen user engagement in the cultural heritage field.

In the era of big and ubiquitous data, professionals and students alike are finding themselves needing to perform a number of textual analysis tasks. Historically, the general lack of statistical expertise and programming skills has stopped many with humanities or social sciences backgrounds from performing and fully benefiting from such analyses. Thus, we introduce Coconut Libtool (www.coconut-libtool.com/), an open-source, web-based application that utilizes state-of-the-art natural language processing (NLP) technologies. Coconut Libtool analyzes text data from customized files and bibliographic databases such as Web of Science, Scopus, and Lens. Users can verify which functions can be performed with the data they have. Coconut Libtool deploys multiple algorithmic NLP techniques at the backend, including topic modeling (LDA, Biterm, and BERTopic algorithms), network graph visualization, keyword lemmatization, and sunburst visualization. Coconut Libtool is the people-first web application designed to be used by professionals, researchers, and students in the information sciences, digital humanities, and computational social sciences domains to promote transparency, reproducibility, accessibility, reciprocity, and responsibility in research practices.

Up to this point, keyword extraction task typically relies solely on textual data. Neglecting visual details and audio features from image and audio modalities leads to deficiencies in information richness and overlooks potential correlations, thereby constraining the model's ability to learn representations of the data and the accuracy of model predictions. Furthermore, the currently available multimodal datasets for keyword extraction task are particularly scarce, further hindering the progress of research on multimodal keyword extraction task. Therefore, this study constructs a multimodal dataset of academic paper consisting of 1000 samples, with each sample containing paper text, images, audios and keywords. Based on unsupervised and supervised methods of keyword extraction, experiments are conducted using textual data from papers, as well as text extracted from images and audio. The aim is to investigate the differences in performance in keyword extraction task with respect to different modal information and the fusion of multimodal information. The experimental results indicate that text from different modalities exhibits distinct characteristics in the model. The concatenation of paper text, image text and audio text can effectively enhance the keyword extraction performance of academic papers.

The field of data curation is rapidly changing due to new developments in technologies and techniques for conducting data work. As the field of data curation evolves, researchers, practitioners, and educators need to be able to respond to these developments. One way to understand trends in a field is by examining published literature. This study first gathered data curation literature through a modified systematic literature review with the framing question, ‘What competencies, skill sets, and proficiencies are needed to conduct data curation activities?’. These literatures were then analyzed using bibliometric analysis, visual analysis of the citation data, and topic modeling to understand trends in the data curation field.

Nostalgia is a self-conscious social emotion that arises from reminiscence of past memories. Collective nostalgia on social media such as Twitter has been seen as a method to comfort individuals in the status of isolation, fear, and a loss of freedom. In recent years, many studies have focused on offering analysis of nostalgic conversations to understand their impact on various domains including marketing and mental health, but little attention has been given to how to detect such conversations in the first place. This paper offers a novel large-scale nostalgic tweets dataset. We describe our extensive methodology to create and validate this dataset using natural language processing models and Large Language Models to detect nostalgic conversations on Twitter. We demonstrate the properties of this dataset alongside analysis revealing insight into context and patterns of what/how people reminiscence about. We finish the paper by describing other research studies that our dataset enables.

Social media plays a vital role as a communication channel for pertinent topics, including climate change. This paper investigates the content and comments of short and long-format videos on Instagram, TikTok, and YouTube to compare climate change discourse on these platforms. Eighty videos and their respective 69,135 comments were collected and analyzed using content analysis, statistical analysis, data visualization, and social network analysis. Our findings show that in our dataset, videos in the short-format (Instagram, TikTok, and YouTube Shorts) provide all sorts of content, while YouTube long videos only focus on educational and activism content. The short-format videos also show different stances towards climate change, such as a climate change denier stance, while YouTube long videos do not. For comments, videos in the short-format had less engagement compared to YouTube long videos. Our findings inform implications for both researchers and social media platform designers.

Tourism live streaming can significantly enhance users’ desire to travel. Although previous studies have identified many technical and social enablers influencing users’ impulsive travel intention, limited research has examined it from a socio-technical perspective. This paper aims to develop and validate a theoretical model that explains how visibility, personalization, and metavoicing influence impulsive travel intention through parasocial relationship in tourism live streaming. Empirical results (N = 551) reveal that visibility, personalization, and metavoicing exert both direct positive impacts on impulsive travel intention and indirect effects through the mediation of parasocial relationship. This paper also identifies gender as a moderating role in the proposed model. This paper enriches the literature by not only shedding light on the underlying process through which users develop impulsive travel intention, considering both technical features and social interaction but also stands as one of the earliest to investigate the gender’s moderating role in this process.

Generative Artificial intelligence (GenAI) chatbots, e.g., ChatGPT, are emerging and gradually becoming pivotal tools in various domains, including libraries. Accordingly, AI literacy definitions, skills, and measures are developed. This research aims to delve into the various factors that are associated with librarians' AI literacy. The study measures included the AI literacy Scale and four sub-scales of AI Device Usage Acceptance (AIDUA): social influence, hedonic motivation, willingness to accept AI usage, and positive emotions toward AI. 107 librarians answered an online questionnaire. Findings reveal that librarians with high AI technology acceptance, which includes social influence, hedonic motivation, willingness to use AI, and positive emotions towards AI, tend to have higher AI literacy, suggesting that these factors can be utilized to promote librarians’ AI literacy.

Pew Research Center's findings reveal a notable trend: U.S. adults under the age of thirty exhibit a level of trust in information on social media that is comparable to their trust in traditional news outlets (Liedke & Gottfriend, 2022). This is particularly significant given the growing concern over misinformation, with a staggering 95% of Americans acknowledging its prevalence in society (Seitz & Fingerhut, 2021). To explore the factors influencing individuals' trust in information, researchers conducted a preliminary review of the literature, focusing on information science. This preliminary review identified seven major themes (health, politics & business, information sources, social factors, age groups, education & language, and privacy & security) that will guide a more extensive systematic review in the future. These themes provide valuable insights into the complexities of trust in information and should serve as a foundation for further investigation. Insights derived from this preliminary review are invaluable for scholars and practitioners seeking a deeper understanding of information trust dynamics. By illuminating these complex factors, this research lays the groundwork for future studies and interventions aimed at enhancing information trust among the general public.

This 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.

While research on how people identify deepfakes are emerging, there is currently a lack of focus on an important age group – seniors. We address this gap through a diary study to elicit seniors’ deepfake identification strategies. Results indicate that seniors performed well in identification performance and over time, the combination of strategies used increased in sophistication. Implications of our work are discussed.

This poster presents results from a phenomenological research study investigating the effects of a university consolidation on the affiliated libraries of three U. S. public university libraries. Semi-structured interviews were conducted through purposive sampling of twelve librarians working at three different consolidated institutions within the same university system. Data was analyzed through the lens of sensemaking theory, a process where individuals attempt to construct meaning and enact their roles within an organization through their evolving experiences (Ancona, 2011; Weick, 1995), and organizational theory which provides both a theoretical and practical way of managing, understanding, analyzing, and improving organizations based on patterns of design and behavior (Daft, 2001). The use of Bolman and Deal’s (1997) organizational theory, including the structural frame, the human resource frame, the political frame, and the symbolic frame, provides insight into the effects on library services and librarians’ identity resulting from consolidations.

The 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.

Public library makerspaces aim to serve diverse groups of populations, but research on how to facilitate the participation of youth with disabilities is very limited. This study presents a case study of an exemplary public library that offers various activities and programming in its makerspaces for youth with disabilities. Our analysis shows different strategies used from the preparation step to reaching out and targeted programming in this case.

AI image generator, also known as Text-to-image generation (TTIG) technology, allows users to generate images based on natural language input. With advancements in AI, debates related to its application in the creative field have flourished. A systematic literature review was conducted to examine discourse that addresses the perception of people in the creative industry towards AI image generators and the impact of AI image generators on these individuals. The analysis of discourse revealed four major categories: 1) Evaluation of the product or outcome of AI; 2) Impact of AI on practitioners/artists; 3) Impact of AI on creative industry and society; 4) Capabilities of AI technology. Evaluation of the product or outcome of AI is discussed most frequently in the discourse, followed by the Impact on practitioners/artists. These findings provide insights into the areas of interest within AI image generator discussions on creative activity.

This narrative review synthesizes knowledge at the intersection of social media scholarship, the role of influencers in disseminating information about feminist causes. Feminist activism on social media, such as hashtag activism like #MeToo, has received much attention from scholars, but the role of influencers in disseminating information remains understudied. Much of the past research has examined the commercial nature of influencers. We conducted a narrative review using the search results from eight academic databases. We examined three research questions: (1) what types of social media influencers have been studied, (2) what feminist approaches were drawn on, (3) and what are influencers’ functions in the dissemination of information on feminist causes. We found that the literature had covered influencers from many parts of the world whose content focuses on various areas of life, with some specifically advocating antifeminism. We also found that feminist theoretical approaches, mainly surrounding neoliberalism and post-feminism, have informed much of this research. Finally, we found that the studies within feminism as challenging norms and expectations, calling out social issues, and building community. From these findings, we derive directions for future studies and the continuation of our project.

This 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.

When a serendipitous discovery is made while searching within a digital library collection (such as an academic digital library at a university), the searcher has a difficult choice to make: either pursue the serendipitous discovery or set it aside and deal with it later. If they take the first option, this breaks the flow of the primary search activity which may make it difficult to resume. If they take the second option, they may have difficulty re-finding what they discovered when they are finished with the primary search activity. We have developed a novel search interface that includes topic-based workspaces and a “read it later” list. Serendipitous discoveries can be easily added to the “read it later” list, allowing the searcher to stay focused on their current search activity knowing that they can easily return to the discovered resource. For each resource saved to the “read it later” list, a textual similarity is calculated against the collection of documents saved in each of the searcher’s workspaces. This allows them to easily identify which of their prior search tasks is a best fit for the discovery, as well as an ability to create a new workspace if desired.

Despite the growing interest in leveraging Large Language Models (LLMs) for content analysis, current studies have primarily focused on text-based content. In the present work, we explored the potential of LLMs in assisting video content analysis by conducting a case study that followed a new workflow of LLM-assisted multimodal content analysis. The workflow encompasses codebook design, prompt engineering, LLM processing, and human evaluation. We strategically crafted annotation prompts to get LLM Annotations in structured form and explanation prompts to generate LLM Explanations for a better understanding of the LLM and transparency. To test LLM's video annotation capabilities, we analyzed 203 keyframes extracted from 25 YouTube short videos about depression. We compared the LLM Annotations with those of two human coders and found that LLMs have higher accuracy in object and activity Annotations than emotion and genre Annotations.

Between 1996 and 2018, there was little federally funded research on gun violence, even though it remains a perennial issue, resulting in considerable loss of life. Advocacy and academic groups piece together data to conduct their research, but the U.S. firearms data infrastructure is flawed. A current project spearheaded by the Inter-university Consortium for Political and Social Research (ICPSR) and researchers at the University of Michigan aims to confront that problem. This Quality Improvement study seeks to uncover the information needs of researchers in firearm injury prevention and community-based interventions through interviews with public health and medical researchers. Interviews with researchers reveal problems associated with established knowledge and data infrastructures. Using infrastructural inversion as a method, we find scattered data and lags in public health data availability. Data harmonization is crucial for researchers and data warehouse managers.

This project explores the intersection of local and online discourse around book bannings using the McMinn County School Board’s decision in 2022 to remove Art Spiegelman’s graphic memoir, Maus, from their school curriculum as a case study. Through interviews with McMinn County residents and data collected from Twitter, I argue that social media has altered book banning discourse. I interviewed 10 residents and asked them to discuss their experiences when Maus was removed by the school board and to reflect on how the local community responded amidst worldwide scrutiny online. Discourse on Twitter involved collecting over 2000 tweets mentioning the @McMinnCountySch account and tweets discussing Maus to understand online responses to the board’s decision. I use Danielle Allen’s discourse flow model as the theoretical framework for this project. In my initial findings, discourse began at the local level before traveling online and then back into the local space. Ongoing data analysis has shown that locally, residents were concerned with the board’s decision along with the perception of their community once news of the banning spread. This is supported by Twitter discourse, where many tweets cast the McMinn County community in a negative light based on the school board’s actions.

Connecting the conference theme “Putting People First: Responsibility, Reciprocity, and Care in Information Science Research and Practice,” this research emphasizes the importance of centering our participants, their experiences, and their well-being. While Information Science (IS) researchers and practitioners aim to understand information seeking and needs across vulnerable communities, this poster suggests using “the vulnerable observer” (Behar, 1997) as a framework to encourage IS researchers to acknowledge and evoke the researcher’s own emotional involvement with their participants. Through the study of vehicle residents’ information practices as a case study, this work draws on various ethnographic methods, paired with information horizon interviews with 22 participants, utilizing trust, reciprocity, and positionality as tools to introduce the researcher’s own vulnerability when working with vulnerable populations. Preliminary findings highlight participants’ communities as valuable information sources some of the most emotionally vulnerable parts of participants’ conversations which impact their information seeking practices. A brief conclusion reiterates the importance of building deep rapport with vulnerable participants.

In 1927, an organized group of Black college and graduate students from the Washington Intercollegiate Club of Chicago compiled a rich informational reference text reflecting the mosaic of Black life in Chicago, IL, USA. This effort resulted in the publication of two editions of the Intercollegian Wonder Book in 1927 and 1929. These books are reflective of the New Negro movement and cultural renaissance of the Black urban population in the early 20th Century. Our project examines the influence of the Wonder Books through methods that express the layered informational context of a Black community. The poster reflects the iterative development of a phased descriptive, archival, data and visual analysis of an historic Black Community. such as citation tracking, genealogical research of individual members, and analysis of data usage by the club's active students.

KEYWORDS

Archives, Citation Analysis, Data Visualization, African Americans, Information History

This poster explores how BookTok, a social media community on TikTok focused on literature, builds a community among an audience of primarily female readers, sometimes called “fangirls.” The researchers analyzed comments on popular BookTok videos, finding that emotional responses, generic comments, and seeking interaction with other viewers were most prevalent. BookTok discussions prioritize shared experiences and emotional connection over critical analysis, creating a welcoming space for readers of all ages. This research suggests that BookTok functions similarly to traditional book clubs, offering a platform for community building and potentially boosting reading engagement and offers possibilities for engagement for libraries and educators.

North Carolina Central University (NCCU) is dedicated to addressing the pervasive issues of digital inequality prevalent in Durham’s underserved anchor communities through the Digital Equity Leadership Program (DELP) and NCCU’s School of Library and Information Sciences (SLIS). DELP offers a holistic approach as its activities and initiatives encompass workforce development, technological access, and digital literacy training, leveraging its partnerships with local organizations. DELP fortifies communities through its holistic initiatives and adopts an innovative approach to fostering long-term community resilience and adaptability in an ever-growing digitized economy. DELP amplifies capacity building through multifaceted employability and economic growth approaches, laying the groundwork for a more equitable and sustainable future. DELP’s core activities expose our community members to skills that support their financial, economic, health, and digital equity. By bringing together program partners and our resources, capabilities, and expertise in digital literacy, innovative remedies were created to reach deeper into the surrounding communities.

This study investigates the role of trust in human-AI collaboration in K-12 education, focusing on generative AI. Using a two-factor experimental design (n = 26), we examined attitudes toward AI and creativity level as independent variables and assessed cognitive trust, affective trust, perceptions of usability, and continuance intention to use generative AI tools in text-to-text (ChatGPT) and text-to-image (Canva) tasks. The results is analyzed with two-way ANOVA, we find that: (1) text-to-text human-AI collaboration tends to foster higher emotional engagement and trust compared to text-to-image human-AI collaboration; (2) perceptions of usability and continuance intention to use are slightly higher in text-to-text interactions, suggesting a preference for text-based generative AI tools; (3) significant interactive effects between attitudes toward AI and creativity were observed across all measured variables, highlighting the importance of considering both factors in understanding and improving human-AI collaboration in educational settings, contributing to research on generative AI for learning assistance.

Climate change is a polarized topic on social media in the U.S. Actors who advocate climate change as scientific fact, or tout it as a conspiracy, both post videos on YouTube. Both kinds of videos can receive millions of views and thousands of comments. Given the polarized nature of the topic, we might expect a high degree of vitriolic speech in the comments around the videos. Previous Twitter studies would also suggest significant differences in the networks made from the interactions of such comments and replies. This study focuses on these comments and replies in an effort to understand the nature of discourse surrounding climate change believer and skeptic videos. Our hope is to extend the existing literature studying scientific communication around climate change, which to our knowledge hasn’t specifically compared discussions around both climate change believers or skeptics on YouTube. Results show most users only comment on other users that align with their own perspectives about climate change, and express positive sentiment toward them. Our study also finds that the more negative the users’ comment, the more connections they have with other users. These findings indicate further investigations of climate change social activities on YouTube.

The current study aims to explore college adjustment and information experiences of first-generation college students in coursework and extracurricular learning contexts. Information World Mapping visual-elicitation interviews were conducted with 25 first-generation college students from two elite universities in one university system. We found that while first-generation college students tend to feel fallen behind, and be somewhat remote from professors and peers, they make use of all resorts to thrive in both coursework and extracurricular learning experiences. Most of their personal-meaningful coursework experiences are academic group work; their extracurricular learning experiences are career-oriented. Implications were discussed based on the findings.

This study is a project designed to be part of the PhD dissertation research as a pilot aimed at assessing the study’s feasibility and its potential for refinement. The paper presents a study that explored the attitudes and knowledge of college students toward privacy in TikTok. This paper aims to comparatively assess student privacy literacy and privacy-related concerns along with their response patterns towards the Florida State University policy banning TikTok on campus networks. The study utilizes semi-structured, qualitative interviews to collect data from five undergraduate students (age 18–24) at Florida State University. It has analyzed and discussed the data collected from these interviews to achieve greater insight into the privacy literacy of students, their privacy concerns, and how Generation Z (Gen Z) students view institutional policies affect their use of social media. Preliminary findings suggests students are skeptical of the ban due to perceived ineffectiveness of the school policy. Analysis showed the new school rules had little effect on students' usage pattern or the amount of time they spent using TikTok.

The introduction of ChatGPT has sparked significant interest and presented both great opportunities and challenges to higher education. While more students are using ChatGPT, little is known about their mental models for and sensemaking of the system, including the metaphors they use to understand the system. In this exploratory study, we interviewed 25 students at three universities in the US during the summer of 2023 to identify the metaphors they use for ChatGPT. Through qualitative analysis of the interview transcripts, we found that participants commonly used metaphors to make sense of ChatGPT. They compared ChatGPT to humans and popular technologies or tools, with being the most frequently Google mentioned. These findings highlight the potential for metaphor-based design and the development of AI literacy programs.

Community 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, along with analysis of the high-level objectives of partnerships, and the array of approaches taken in pursuit of those objectives.

Modern democracy often requires citizens to engage in policy debates demanding multidisciplinary knowledge. This can lead to a cognitive gap that urges citizens to seek information for decision-making on unfamiliar policy issues. The credibility of this information is crucial, as it shapes individual perceptions and attitudes, which aggregate as public opinions. To deepen the understanding of information credibility, this study proposes an experiment leveraging theories of the continued influence effect and the illusory truth effect to answer how initiative and repetition of exposure influence the evaluation of information credibility. 1,400 participants will be recruited and requested to rate the credibility of opposing arguments regarding a fictitious referendum provided with varying orders and numbers. The result is expected to show a positive causality between exposure initiative, information repetition, and information credibility. The findings will aid in promoting well-informed democratic deliberation in an era of political polarization.

Although the discussion about conversational agents is inevitable, the question arises whether this theoretical and practical topic is starting to roll within the Library and Information Science (LIS) community. This poster submission is interested in providing a first insight into the current LIS research activities, focusing on the publication sources selected by the authors. For this purpose, the databases Scopus and Scimago Journal & Country Rank (SJR) were used. Of the initial LIS publication sources (N=271), about 27% of the sources accepted research articles by authors are about conversational chatbots.

One of the key aims of information search tools is to organise information and make it accessible and useful, reducing the chaos of an information deluge to ten blue links, perhaps with some context about the utility of the information they represent. Generative AI answer engines, such as ChatGPT, have gone one step further, reducing the ten blue links to a single passage of text: highly digestible, easily accessible, possibly false and ultimately bland. All chaos, including disagreement between sources, possible false information, and the influence of societal biases, and the opportunity to access more information is hidden under a rug of simple, authoritative sounding text. Friction, disagreement, reflections and odd informational juxtapositions have long been known to underpin serendipity, inspiration and creativity, yet these answer engines reduce all of these opportunities, particularly in the context of complex information needs. Using 'bounded chaos' as a framework, we critique the trade-offs made by answer engines, demonstrating that, even as they are non-deterministic, they hide the wrong parts of the chaos for those who seek learning or inspiration. We make the case against bland, and defend information seekers need for perceptible heterogeneity, friction, and even a little chaos.

The 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.

Research teams naturally appear from long-term collaborations. However, effectively tracking their evolution, showing cross-team participation, and defining team activity periods pose significant challenges. To address these issues, we propose an innovative method involving the computation of an annual active index for each researcher. We applied our method to analyze the solid-state battery domain. Leveraging co-inventor relationships, we found 246 teams, categorized into three groups based on their size. Approximately one-third of these teams drive technological development, with more than half of the members taking part in multiple teams simultaneously.

We 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 will be used to help us better understand how contextual factors shape HI and contributes to mixed methods in information science research.

Many of the biggest issues facing public health today focus heavily on information, yet few information scientists work directly on applied public health problems. While health informatics has a rich history in LIS, for information scientists who want to work on applied public health issues, mentorship has been ad hoc and cross-training pathways individually forged. With funds from the Public Health Agency of Canada and Canadian Institutes of Health Research, the Public Health Information Science Initiative (PHISI) will increase information science capacity in applied public health internationally. The PHISI will involve three one-year mentoring cohorts of emerging information scientists interested in conducting applied public health research, with an emphasis on population health information intervention research Initial plans are to hold a pre-conference training workshop to launch each cohort, with webinars and individualized mentoring throughout the year. By taking the mutual mentoring approach identified as especially effective for supporting under-represented early-career scholars, the PHISI will benefit both early-career information scientists and the established public health and information scientists who participate. This poster provides an interactive opportunity for conference attendees to make suggestions, ask questions, and directly influence the shaping of the first PHISI cohort.

The introduction of the Association of College and Research Libraries’ (ACRL, 2016) Framework for Information Literacy for Higher Education, or “the Framework,” has fostered efforts to apply the document’s concepts to information literacy instruction (ILI) to meet the needs of post-secondary students across faculties. However, little research has directly compared students’ perspectives on the Framework’s concepts by discipline. This poster presents findings from a larger, mixed-methods project investigating student perspectives on the Framework in which participants in the faculties of arts (n=123) and science (n=90) expressed distinct views and rankings. Their perspectives particularly diverged around perceived roles in assessing the authority of information sources and how “relevant to their experience in school” they found most Framework frames. This project expands upon emerging learner-centered research aiming to meaningfully improve Framework-based ILI through its direct disciplinary comparisons and use of the recently developed Information Literacy Reflection Tool (ILRT; Robertson et al., 2022).

Even 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.

This study investigates various types of situational relevance as assessed by students in the context of AI-generated responses for educational purposes. Data were collected through interviews with four undergraduate students, and a qualitative research method was employed to analyze data. The findings reveal the specific roles of AI chatbots concerning students' tasks and types of situational relevance. The research highlights differences between system inefficiencies and students' coping behaviors in situational relevance types, providing implications for future research.

Canvas, a learning management system (LMS), has been widely adopted by academic institutions across North America. Despite its robust features, the user experience with Canvas varies significantly. This study examines the design of a Canvas course website, providing evidence-based guidance for creating user-centered LMS interfaces. The findings aim to enhance the user experience by addressing issues such as information overload, user control, and accessibility. Design principles and recommendations generated from this study are crucial for our institution-wide adoption of Canvas, promoting a seamless and supportive learning environment.

Medical evidence on the risks and benefits of cannabis is limited; existing research is often inconclusive or conflicting. In the United States, cannabis use during pregnancy is stigmatized and often subject to complex legal constraints; these contextual components may have significant effects on information seeking and informed decision-making processes. This study applies reflexive thematic analysis to 23 telephone interviews with individuals considering cannabis use in pregnancy or lactation to explore their information needs; how adequately those needs were met; and how information triangulation, and other information seeking behaviors, were used to make decisions given the dearth of scientific evidence. Findings suggest that information needs are complex and contextual, and that participants used forms of triangulation that included relational and intuitive elements as well as cognitive assessment processes. A new model of Everyday Triangulation (ET) is presented to represent these complex assessment practices in a holistic manner.

Traditional roles of Japanese prefectural libraries involve supporting and advising municipal libraries. However, since the 2000s, their roles have diminished owing to financial downturns, an aging population, and municipal libraries’ establishment, forcing them to redefine their missions and services to remain relevant. This study aimed to determine whether differences exist in the services of prefectural libraries based on the varying status of municipal libraries and to illustrate these differences through case studies on a pilot basis. The following three research questions were posed: (1) Do differences exist in the services provided directly to citizens? (2) Do differences exist in the services provided through municipal libraries? (3) Do differences exist in the services supporting municipal libraries? Using hierarchical cluster analysis, Tokyo and Hokkaido were selected for a comparative case study. The findings indicate significant differences in services, influenced by establishment rates and service levels among municipal libraries.

Brenda Dervin’s Sense-Making Methodology has had a significant impact on communication, information science, and other fields, with over 18,800 citations on Google Scholar. Researchers over the years, including a recent ARIST review, have studied Dervin’s work. However, prior research has not analyzed a single article to see how it has been cited over the years across different fields. In this study, we examine 50 of the 1,766 articles published after the year 2000 that cited her 1983 paper. The research question investigated is, “What is the impact of Dervin’s Sense Making Methodology in the twenty-first century in fields outside of information science?” We found that sense-making has been applied to social media, academia, and health information seeking, among other related fields. The findings should be useful to scholars interested in information science and methodology. The study also shows that information science is not a siloed field and impacts other fields.

Digital interfaces serve as gateways to information, and web accessibility is the key to fostering equity in these spaces. This paper comprehensively analyzes web accessibility on library webpages across Connecticut public colleges (USA). With the most recent publication of Web Content Accessibility Guidelines (WCAG) being published in 2023, there is limited research available about how academic libraries meet these new standards. By employing a systematic methodology, this study examines the accessibility features and barriers presented on these webpages. The research was completed using manual checks for the 80 of the 86 WCAG 2.2 level AAA standards. The quantitative data was analyzed to provide a nuanced understanding of the current state of digital inclusivity in academic library websites. The findings of this research shed light on the prevalent accessibility gaps and offer insights into potential strategies for enhancing web accessibility practices for academic institutions. This study contributes to the ongoing discourse on digital inclusivity and underscores the importance of prioritizing accessibility initiatives to create more equitable digital environments for all users.

The newest editions of generative AI (GenAI) tools demonstrate profound capabilities that promise to reshape education. It is of vital interest to determine how these applications will modify how the public perceives and interacts with history, as well as how history education is delivered. In this poster, we investigated academic historians’ practices and perceptions of integrating GenAI into their teaching. We conducted semi-structured interviews with six historians who teach university-level courses. We identified the major concerns and perceived benefits of GenAI integration in history education, and some typical uses of GenAI in history classes. Our work contributes an important baseline assessment of attitudes and practices within the field so that we may better understand the existing gaps among academic historians and the extent of those gaps.

3D data creation methods and virtual reality (VR)-based visualization techniques are increasingly common in interdisciplinary research and pedagogy. 3D data represent a new challenge to existing digital curation frameworks for academic libraries and other support institutions. This poster describes the research methodology, background, and research plan for an in-progress, 3-year project funded in part by the Institute of Museum and Library Services (IMLS) (grant #: LG-254830-OLS-23). This project is exploring 3D data creation and curation practices of researchers across a range of 3D creation modalities and disciplines.

In cultural heritage crowdsourcing, imbalanced volunteer task choices can impact project completion rate and sustainability. Nudges offer a way to alleviate this imbalance. This paper proposes three nudges: task order, visual saliency, and feedback based on dual-process theory. We conducted a 2×2×2 factorial experiment incorporating eye-tracking research methods to investigate how these nudges affect volunteer task choices. The results demonstrate a significant positive impact of visual saliency. While task order and feedback did not demonstrate significant effects, eye-tracking results reveal that they effectively captured participants' attention. Our findings suggest that designing platforms with nudges could address the issue of imbalanced task choices in cultural heritage crowdsourcing projects.

Despite the growing emphasis on artificial intelligence (AI) education, there is relatively little research on the motivational factors that influence students' intention regarding AI knowledge acquisition and the utilization of AI applications. Understanding these factors helps educators and researchers to develop appropriate interventions to promote AI education. Guided by expectancy-value theory and theory of planned behavior, we examined how university students' beliefs influenced their motivation to learn about and engage with AI technologies. Our findings demonstrated the significant role of expectancy-value beliefs in shaping students’ behavioral intention. Additionally, we identified gender differences, which can inform educators in designing gender-specific interventions to enhance female students’ motivation in AI learning.

This 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.

Globally, approximately 50 million people live with dementia, a number projected to exceed 152 million by 2050 according to Alzheimer’s Disease International. Recent studies reveal that individuals with dementia, including Alzheimer's disease (AD), exhibit positive responses to familiar music, potentially reducing agitation, depression, and stress. However, traditional methods of identifying music preferences in dementia patients, such as questionnaires and interviews, are cumbersome. This paper explores a novel approach utilizing a minimal user-interface music player prototype for smart devices. Using the music player prototype, facial expressions of participants (Germany, and South Korea) age 65+ were recorded while listening to music to detect a song preference. Although participants did not have diagnosed dementia, they exhibited age-related memory loss, serving as proxies for testing. Results suggest the feasibility of automated music preference detection, eliminating the need for traditional input methods like personal information or keyword searches.

Individuals with polycystic ovary syndrome (PCOS) turn to Instagram influencers as an alternative or complementary PCOS information source to their physicians. However, ambiguous qualifications of influencers, and contradicting claims regarding how to manage the condition, make it difficult to identify credible sources. Using a directed qualitative content analysis, we examined the range of strategies that 10 Instagram PCOS influencers used to signal credibility. We found that influencers utilized three source credibility strategies. A key contribution of the study is a description of how PCOS influencers reference sources to signal credibility. Following a discourse analysis, we found that Instagram PCOS influencers discuss what it means to be healthy with PCOS, and how they contribute to a wider cultural participation in healthism. The findings build upon the source credibility literature to identify novel credibility cues used by influencers as social media platforms evolve, and further, reveal the underlying healthism-centric rhetoric by PCOS influencers.

The study proposes and describes a novel system called the Health Data Exchange. HDX is useful for identifying relevant experts in healthcare based on specific skill sets and their cohorts. Initial experience suggests that an expert database based on biographical data can be developed. Several techniques for automatically identifying the critical data, data normalization, storage, and retrieval are described that supported the development of the system. Such a system has the potential to facilitate efficient search and foster global collaboration among healthcare experts.

Despite global recognition of health as a fundamental human right, disparities persist due to gaps in Health Insurance Literacy (HIL). This study investigates HIL among young adults, focusing on their search behaviors and the impact of Search Generative Experience (SGE) and Generative AI. Participants displayed skepticism towards sponsored ads, preferring unbiased sources on government websites. Brand familiarity, notably with Blue Cross Blue Shield (BCBS), also influenced decision-making. Engagement with SGE for simplified explanations suggests its potential to impact HIL. Transparent and user-centered interventions are crucial for improving HIL, contributing to more informed healthcare decisions and reduced disparities in access to care.

This study evaluates the usefulness of heatmaps in understanding user preferences during book selection in online book discovery platforms. Based on user interactions with two book overview pages, it compares heatmaps generated from user clicks and eye-tracking data to identify key metadata influencing readers’ choices. Initial results indicate that click-based heatmaps can be a useful tool for this type of investigation, especially considering the cost of eye-tracking where heatmaps are typically generated.

This poster investigates how ACRL award-winning libraries address generative AI literacy in their libguides. Using the ACRL "Framework for Information Literacy for Higher Education," we analyzed newly created guides from 28 libraries. We found that this framework could serve as a foundation for AI literacy. The coding scheme developed from this analysis can be used as a reference for librarians when planning instruction or creating libguides. In addition, we identified common strategies and literacy components that librarians emphasize at this stage.

The 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.

Machine learning (ML) models for deepfake detection are important for countering the threat of such videos. However, human detection is also critical because automated approaches may not always be available to people online. This study compares ML models versus humans for deepfake detection, using a selection of real and fake videos obtained from the Web. Results surprisingly showed that humans performed better. Implications of our work are discussed.

In academic literature, the semantic evolution of scientific terms reflects the development of domain knowledge. While there have been studies using diachronic word embeddings to reveal cultural and social changes, research on the semantic evolution of scientific terms remains limited. This paper proposes a method utilizing diachronic semantic vectors to elucidate the semantic evolution of scientific entities. Firstly, diachronic semantic vectors of entities are trained using incremental learning to compute the semantic transition (ΔA) of a given entity A between periods t1 and t2. Secondly, entities similar to ΔA in periods t1 and t2 are defined as leading reasons and accompanying reasons, respectively. The experiment result shows that the leading reasons can capture the changes of entities in the nascent stage, and the accompanying reasons can capture the changes of words in the mature stage. The proposed method offers a better quantitative insight into the details and reasons behind domain knowledge evolution.

Prior research suggested the empirical approach to indexing for fine arts based on users’ search queries. This study examined the characteristics of user queries submitted to the library catalog of a fine arts academy, and compared users’ search terms with controlled vocabularies to assess their levels of matching. Based on search-log analysis, this study identified eight categories of user queries and 10 subcategories, indicating that the information needs of the fine arts community are diverse in language and culture and focus on named entities, disciplines, and various types and levels of fine arts. The mapping between user queries and controlled vocabularies found that around 20% of user search terms were non-matching. Based on the findings, this study provided suggestions for improving the search functions of library catalogs and enhancing controlled vocabularies to support the retrieval of fine arts monographs.

This study aims to compare the information literacy education practices of university libraries in Taiwan and South Korea using the Framework for Information Literacy for Higher Education (ACRL, 2016). The result shows that while Taiwanese institutions demonstrate comprehensive coverage across all six frames, South Korean university libraries lack some frames. The identified gaps will be analyzed thoroughly with ongoing research. This research will contribute to understand the shortcomings in the information literacy education provided by university libraries and suggest how we can further research and improve in the future.

Environmental sound recording is increasingly vital to the production of scientific and ecological information and knowledge. While research applications have grown tremendously, there is a lack of empirical research around the practices and experiences of those responsible for creating and managing these recordings. This poster reports on initial findings of a qualitative research project that interviewed 30 academic and professional researchers from across Canada and the United States, exploring information activities and meaningful elements and actors involved in their interactions with environmental sound recording. From preliminary findings, two themes are discussed: sound and listening skills involved in the creation and analysis of environmental sound recordings are complex, multisensory information practices, and changing environments and technological affordances enable and constrain the seeking and sharing of information and resulting knowledge production.

This research examines how female college students interact with various types of information in the social practice of job seeking in Japan. Qualitative analysis of semi-structured interviews with female college students reveals how they sift through abundant information during this process, which puts them under considerable pressure. Considering their prospects as women in the workplace adds another layer to the factors influencing their interaction with information during job search. Their information activities involved internal mediation between the information and experiences they gain and their own understanding of themselves regarding the goal of their job search, which can change over time. They come to terms with their results by making sense of or accepting their decisions. This study offers practical insights for designing more effective support for students.

Given the increasing prevalence of patrons with diverse social, physical, and psychological needs, public libraries are beginning to explore ways to address these community needs by offering social services. However, questions remain about whether social services should be part of the library's service domain and how libraries can effectively fulfill these needs. This poster presents insights from community members and librarians on the relevance of integrating social services into library missions, offering implications for a potential library social service.

Early publishing, or preprints/e-preprints, is the practice of making research available before formal publication. This research examines early and non-early publications in Library and Information Science, focusing on trends, citation impact, obsolescence, and time lag. It reveals an upward trend in early publications, which, despite their lower quantity, have a higher citation impact. Early publications also demonstrate a longer citing half-life, indicating their sustained relevance. Analysis of time lags reveals that about 49.25% of early publications are formally published within three months, with 75.5% published within six months, and only 5.66% taking more than a year. These findings underscore the importance of early publication in enhancing research impact and visibility.

This paper introduces innovative participatory methods designed to foster community dialogue and engagement in development initiatives. We present participatory mapping, participatory photography, and two culturally grounded games, "Mayuk" and "Cachiveras," as tools to facilitate community participation, dialogue, and reflection. Grounded in the theory of participatory action research (PAR) by Orlando Fals Borda, these methods prioritize local knowledge, foster a dialogue of knowledge between communities and researchers, and aim for social transformation through active community participation. By implementing these participatory activities, we not only engage communities in identifying resources, strengths, and opportunities but also promote a playful strategy for adults to deepen analysis and engage in critical dialogue about their culture and communication. This approach aligns with the conference theme of Putting People First, demonstrating responsibility, reflexivity, and care in the creation and evaluation of participatory development activities.

Infrastructure consists of interlinked and nested sociotechnical systems, the construction, maintenance, and repair of which requires considerable ongoing outlays of material resources, technical know-how and throughput, and political will. Building upon “broken world” approaches, this research uses qualitative methods to study infrastructure development, breakdown, and repair through the lens of a particular collaborative humanitarian initiative involving the public health sector in the Gaza Strip. Findings from this research suggest that a lack of donation ethics combined with policies that privilege “service over stuff” enable and exacerbate key infrastructural breakdowns, perpetuate maladaptive behaviors, inhibit meaningful development, and reinforce dominant harmful regimes. The paper concludes by arguing for increased critical and ethical awareness of donation culture and its consequences, a reconsideration of development policies that limit funding support to services rather than material and infrastructure supports, and an emphasis on building local capacity for maintenance and repair work.

Video games are both information objects as well as experiences of play. However, current methods of game preservation often fail to capture the full gaming experience. To address this, one proposed solution involves leveraging player-generated content as an alternative to emulation. To investigate the viability of this suggestion, we examine the following research questions: (1) What types of gameplay content exist on the platform YouTube? and (2) What are the challenges and opportunities in utilizing this style of community created content for game preservation? We introduce a Taxonomy of YouTube Game-Related Content to answer these questions and discuss its application, along with the challenges and opportunities that arose during its creation. Our taxonomy and findings suggest that although utilizing this content for game preservation requires additional knowledge of games and their community, as well as consideration of how the platform and creators impact game history, it also presents opportunities to preserve more diverse voices and experiences of play. Furthermore it suggests YouTube videos offer a potential avenue, not only for preserving the game object, but also for preserving its broader cultural significance and multiple forms of knowledge.

Mobile map applications greatly facilitate users' everyday information practices and provide them with real-time and accurate information in different contexts. However, few studies have explored the contextual information needs of users in their interactions with mobile maps. Semi-structured interviews with 18 participants revealed that users present diverse and rich information needs in their everyday use of map applications, and in addition to the most common information needs for situ navigation, users' contextual information needs varied. Particularly, mobile map applications are increasingly characterized by serious leisure while highlighting their utility. In addition, with the evolution of the platformization of map applications, users are also demanding more autonomy, visibility, and security. This study contributes to the literature on human-information interaction in mobile maps and sheds light on the user experience design of mobile map applications.

Disruptive technologies such as ChatGPT and similar Large Language Models (LLMs) have transformed mundane everyday tasks of information users since their debut in late 2022. In this study, we leverage uses and gratifications theory to test a distinct set of motivations that drive users’ satisfaction and continued use intentions of ChatGPT and similar large language models. Data were collected using a national online survey of 323 adults residing in the United States. We conducted data analysis using Partial Least Squares (PLS-SEM) to investigate both direct and indirect impact of factors on users' gratification, thereby influencing the continued utilization of these tools for everyday information seeking. Results show four motivational factors - social influence, trust, personalization, and perceived usefulness - that positively influence users' satisfaction or sense of gratification, impacting their intentions to continue using these tools. This is one of the few early studies of ChatGPT and other LLMs from an information science perspective.

The proliferation of Artificial Intelligence Generated Content (AIGC) poses significant challenges to user experience and information accuracy, especially on search engine websites. The current solution is to identify AIGC by machine learning algorithms or publicly available AI detection tools, whereas, machine learning algorithms degrade in accuracy as more data is available and tools such as GPTZero perform poorly in the task of AIGC detection on social media. In this paper, we propose an EPCNN approach to identify AIGCs in search engine websites, which maintains good performance in large-scale samples. The ERNIE model integrates cross-domain knowledge and improves language understanding and generalization. We use ERNIE to extract text features, then use a feature pyramid network to capture semantic information at different levels, and finally use an end-to-end structure to connect ERNIE and the feature pyramid network to construct the EPCNN. Experimental results show that our proposed algorithm has high accuracy and the ability to handle large-scale data compared with machine learning algorithms and AI detection tools.

This study conducted a systematic review of human-AI interaction (HAI)over the past decade for the implemented theories and scenarios, and the tested hypotheses to discover the changes in the current transition to human-centered AI (HCAI). Moving from acceptance theories, Computers are social actors (CASA), anthropomorphism, and the integrative trust model are the most frequent theories. Augmentation scenarios of decision-making, teamwork, and human-AI collaborations are common in the latest HAI studies. Users' trust, acceptance, and intention to use an AI system are the main research targets in HAI studies. These trends show a clear transition toward HCAI. This paper also discusses opportunities tied to HAI studies based on the interconnections between the various theories, scenarios, and hypotheses.

The development of generative AI and Large Visual-Language Models, has the potential to overcome the reliance of blind or visually impaired (BVI) persons on human sources for visual information. Action research and semi-structured interviews were conducted with 19 BVI persons to explore their visual information needs and source selection. This study categorizes their needs into description, navigation, and manipulation. Accessibility, credibility, and interactivity of information sources are the primary criteria for BVI persons in information source selection. When visual task demand high accurate information, BVI persons are more likely to select human information sources due to their accessibility and credibility. When tasks are less urgent and requiring less accuracy information, the use of AI information sources increases due to the special accessibility. The purpose of the study is to understand the impact of AI technology on BVI persons, and provide theoretical and practical insights for the development of human-centered AI.

Parents’ concerns about children’s digital media use were investigated using data from semi-structured interviews with 17 parents of children ages five to 11 at three branches of a U.S. urban public library system. Data were analyzed using collaborative inductive thematic analysis and approached with the concept of culturally-constructed anxieties about new media as an analyzing lens. The most common concerns included worries about exposure to inappropriate content, worries about digital media taking up time that children would otherwise spend engaging in more meaningful activities, concerns about safety and privacy, and worries about negative effects on children’s behaviors, attitudes, and social skills. Further analysis showed parents' deeper concern for children’s healthy development to underlie these narrower concerns. The authors conclude with the recommendation to shift the framing of discourse around parenting with digital media from risk protection to digital media education. Such a shift could raise awareness that framing children and digital media only in terms of risks is overly simplistic, and it could help parents come to understand that children’s digital media use is not just risky but also an opportunity for children to derive educational and social benefits, and learn how to operate in a digital media-dominated information ecosystem.

Virtual storytime programs supported young children and families when in-person library services were discontinued in response to the COVID-19 pandemic in the United States. These virtual storytimes offered via videoconferencing systems allowed for the inclusion of some in-person storytime elements such as the librarian read-aloud, and children are the target audience of these picturebook read-alouds. Children’s nonverbal behavior symbolizes a powerful clue as to when they are engaged with the read-aloud story. What is unclear is how a child nonverbally engages with the storytime readings. This study shows how human pose detection software, OpenPose, was instrumented with custom Python scripts to track and code a preschooler’s nonverbal behavior during a virtual storytime read-aloud. Results demonstrate considerable nonverbal attentiveness and engagement by this child during the read-aloud.

Accessibility education has been rarely incorporated into the high school curricula. This is a missed opportunity to equip next-generation software designers and decision-makers with knowledge, awareness, and empathy regarding accessibility and disabilities. We taught accessibility to students (N=93) in a midwestern high school through empathy-driven games and interviewed three Computer Science high school teachers and one librarian who taught programming. Accessibility education is currently insufficient in high school, facing challenges such as teachers' knowledge and conflicted curriculum goals. The students exhibited increased knowledge and awareness of accessibility and empathy for people with disabilities after playing the games. With this education outreach, we aim to provide insights into teaching next-generation software designers about accessibility by leveraging games.

The Cognitive Map Mechanism explains that different textual representations influence cognitive effects by affecting how readers mentally organize and navigate information. The application of this mechanism in digital reading remains underexplored. We conducted a lab experiment(N=148) manipulating low/medium/high textual representation through non-/micro-/combined-annotation modes. Our aim was to clarify how the cognitive map based digital reading variations impact reading comprehension, performance calibration, and reading speed. Sufficient results showed that high-level textual representation provides superior cognitive effects in all dimensions. It is interesting to discover that medium-level textual representation impaired cognitive effects compared to low-level representation. Specifically, metacognition negatively moderated the influence of textual representation level on reading speed, suggesting that texts with both micro-annotations and graphic organizers can provide comprehensive cues and visual correlations to build cognitive map, thereby enhancing cognitive effects. Our study contributes novel insights for future research and practical implementation of cognitive-oriented strategies in digital context.

This study examines the extent to which TikTok’s privacy policies and app settings in the U.S. and Europe protect the rights entailed in the data subject-centered framework. Using a case study approach, we analyze current policy documents and app settings to identify the alignment of TikTok’s policies with the GDPR perspective. Our findings reveal that current policies and settings fall short in key areas. First, TikTok policies lack details related to managing and protecting sensitive data. Second, the policies neglect to discuss the responsibilities of social media companies when such data is utilized by unspecified third parties. Furthermore, there is a noticeable deficiency in the U.S. regarding detailed in-app privacy notices and setting options, especially in terms of managing location data and advertisements. Additionally, there is a need for explanations on how specific settings impact users. Lastly, a critical demand exists for default settings, including those for advertisements, to enhance data protection.

Increasing use of artificial intelligence (AI) and big data in surveillance by the U.S. immigration enforcement has led to important ethical considerations. This paper aims to explore the extent to which immigration lawyers are aware of surveillance technologies in their work with immigrants and the lawyers’ potential concerns about surveillance technologies. Through a thematic analysis of six semi-structured interviews with U.S. immigration lawyers and legal practitioners, this research reveals three overarching themes that describe lawyers’ perspectives on surveillance: surveillance knowledge, surveillance assemblage, and surveillance implications. These themes are rooted in lawyers’ understandings, experiences, and encounters with surveillance in their daily work with immigrant communities and contribute to existing work at the intersection of surveillance, information studies, and immigration. This work calls on scholars to explore and expand on understandings of surveillance within U.S. immigration enforcement.

While considerations of documents and data are longstanding in the tenants and practices of library and information science (LIS), the recent turn toward bodies and embodiment in the social sciences invites a critical interrogation of our assumptions about the interplay of documents, data, and bodies embedded within sociotechnical systems of power and bodily agency. In response, we begin to theorize the intersection of datafication and documentation as documentification, encapsulating how acts of datafication revoking agency results in a one-directional superficial documentary status, producing assumptions about bodies by power systems which aim to simplify, nullify, and suppress. We initially examine documentification as it relates to practices of surveillance, BMI, and memory institutions. In doing so, we interrogate the ethical dilemmas emerging from assumptions about agency ascribed to documentified bodies. Finally, we challenge the library and information professions to imagine a world designed with putting people first that centers, rather than reduces, their agency.

Effective data exchange holds the potential to bridge information gaps between government agencies, creating essential prerequisites for enhanced collaboration. However, consistency issue often hinders the performance of interagency government data exchange. Inconsistent query results may be returned from different databases if data records are not timely synchronized, degrading mutual trust and collaboration efficiency among agencies. To address this issue, an interagency government data exchange approach is proposed. Specifically, consortium blockchain is leveraged as a write-ahead log, enabling different agencies to trace relevant requests failed to be executed in real-time, thereby promptly providing consistent query results. Detailed settings on protocol level are designed for the blockchain platform to facilitate data exchange regulation, including data structures, consensus algorithms and access control mechanism. Extensive simulation experiments are conducted to evaluate the performance of the proposed approach and investigate the impact of different parameters on data consistency and system availability.

Generative AI (GenAI) impacts the ways we create, engage with, and understand creative and intellectual works. These new forms of sociotechnical (inter)action pose challenges for existing legal regimes, ethical frameworks, and social relationships. This research undertakes an in-depth copyright analysis of GenAI based on U.S. law, focusing on its fair use doctrine and conceptions of transformation. This work finds that courts’ characterization of uses as primarily either “expressive” or “mediating” is an important, though often implicit, factor in their decisions. Furthermore, while “transformative use” has dominated fair use decisions for the past thirty years, findings from this research suggest that GenAI may usher in a renewed emphasis on the doctrine’s market harms element which, in application, may be dispositive with respect to GenAI outputs. This work concludes by offering recommendations aimed at clarifying that the value of copyright arises from social and relational aspects of creative practice and sociotechnical transformation. Arguments and rationales that (over)emphasize atomization and algorithmic decontextualization of the material properties of creative works are unlikely to attend to the underlying purpose of the Act: “[t]o promote the Progress of Science and the useful Arts”.

In 2022, almost 50,000 people died in road crashes in the United States, with speeding implicated in 29% of these fatalities. Despite known links between vehicle speeds and crash occurrence and severity, there are no federal guidelines for collecting vehicle travel speed (VTS) data. Cities with open VTS data are using unstandardized datasets, which complicates large-scale and cross-jurisdictional analysis. We conducted a qualitative assessment of open data repositories for the 25 largest U.S. cities, using a framework of knowledge representation, evaluated twelve metadata components, and determined the potential usability of these datasets. Our knowledge representation framework includes five data elements: speed metric, timestamp, geospatial representation, posted speed and vehicle type. Findings show that one-quarter of these cities have open VTS datasets. Of those cities, none has a VTS dataset containing all the elements defined in our framework. This suggests the need to design information policy standards for the collection and sharing of open VTS data.

Lifelong learning involves and engages learners at all stages of education and life. In particular, learning transitions, which represent phases when students advance to the next stage of an educational setting, are significant as they may trigger learning and development for lifelong learning. Research suggests that the learning transition process provides an opportunity to develop learning abilities that have long-term consequences, including lifelong learning. The notion of information practices is thus applicable to examine the development of these abilities, as information practices refer to the routine ways in which students seek and interact with information to manage the changes in the information environment during the transition. Despite the significant role of information practices during transitions, limited work has been done to identify information practices that facilitate lifelong learning for students in learning transition. To fill this gap, this study conducted focus group discussions with forty-two university students to investigate their transitions to university via the lens of information practices. A preliminary thematic analysis was conducted. Information use and evaluation were uncovered to shed light on the influences of information practices on lifelong learning during learning transitions.

In the context of global aging, digital technologies are increasingly recognized as a means to address the challenges associated with aging. Although many studies have explored the relationship between older adults and digital literacy, there is a gap identifying their necessary skills, challenges, and learning motivations. This review identified 30 articles from 2014 to 2024 and delineated five areas of digital literacy skills that older adults acquire, the hurdles they face in this process, and what drives their learning efforts. The difficulties faced by older adults in learning digital literacy include unfamiliarity with technology, cognitive decline, emotional barriers, limited access to digital resources, and physical and health issues. These challenges may lead to frustration in their use of devices and apps, which may affect their willingness to learn. This study, despite a small sampling size, paves the way for future research, including evaluating online fake information and addressing safety risks.

This paper explores an approach for applying Artificial Intelligence (AI) to co-design smart hand tools to personalize learning for future skilled trade workers in workforce training programs. The purpose of this research is to better understand the perspectives of workers in the skilled trades and to respond with co-designed socio-technical interventions that empower workers. The research benefits from a collaboration between The University of Texas at Austin, the City of Austin, and Austin Community College (ACC) and incorporates insights from welding instructors and students, as well as skilled trade workers and supervisors. Social science findings derived from semi-structured interviews inform tool design implemented by an interdisciplinary research team. The participatory design approach has resulted in two prototypes: a welding simulator that uses Augmented Reality (AR) and an AI-enabled (smart) rotary tool. This paper has implications for workforce development to address skilled worker shortages. Additionally, it contributes to ongoing research into AI and skilled trade work which is understudied compared to AI and knowledge work.

Digital inclusion interventions are designed to address challenges related to digital technology access and use. The complex relationships between digital technologies and people’s general well-being is important when considering the development of digital services, practices, policies, and interventions. This research explores the impact of digital inclusion on individual agency and provides evidence of how agency emerges from the use of digital technologies to contribute to well-being. We present a framework of Digital Inclusion for Well-being drawing on evidence from interviews with participants of three digital inclusion interventions in Aotearoa New Zealand. While recognizing that digital inclusion is a continuum and that some of our research participants were more digitally engaged that others, we propose a preliminary framework of digital inclusion connecting our participants’ digital engagement with outcomes impacting their agency, and thus their well-being

As technologies powered by Artificial Intelligence (AI) and Machine Learning (ML) algorithms increasingly take over personal computing online and public sector domains, they simultaneously raise the promise of an extensively productive and sustainable future, as well as fears of widening inequalities, information and content divide, and a more complex information-seeking landscape. Thus, the hopes of improved accuracy, efficiency, productivity, reduced human bias in decision-making, and access to information are fast giving way to a trove of ethical and human rights issues with far-reaching consequences for accountability, privacy, social justice, equity, inclusion, and informed consent, and public participation in decision-making. Since no technology is entirely free of bias, this paper identifies algorithmic fairness as a more realistic threshold and goal. Building on findings from a previous PRISMA review of relevant literature, the paper proposes a comprehensive framework for defining algorithmic fairness in the context of information access.

This paper reports on an audit study of generative AI systems (ChatGPT, Bing Chat, and Perplexity), which investigates how these new search engines construct responses and establish authority for topics of public importance. We collected system responses using a set of 48 authentic queries for 4 topics over 7 days, and analyzed the data using sentiment analysis, inductive coding, and source classification. Results provide an overview of the nature of system responses across these systems, and provide evidence of sentiment bias based on the queries and topics, and commercial and geographic biases in sources. The quality of sources used to support claims is uneven, relying heavily on News and Media, Business and Digital Media websites. Implications for system users emphasize the need to critically examine Generative AI system outputs when making decisions related to public interest and personal well-being.

By examining loan data from public libraries in South Korea, we seek to understand patterns in user borrowing behaviors and explore thematic connections among borrowed books. The subject headings of 55.5 million book sets borrowed by individual users on the same day were analyzed using ITEM2VEC. We have identified 40 subject heading communities through cosine similarity of each subject vector, and we have labeled each community using a large language model. Two prominent communities were identified: Global Modern Literature and Novels and Children’s Literature, Fairy Tales, and Folklore. The latter community was associated with a diverse array of subjects, indicating an expansion in children’s reading preferences. The study results will be useful for improving collection development and the relevance of book recommendations, as well as for incorporating user information behavior into traditional library material classification schemes.

Open access (OA) publishing has been touted as an equalizer to access. However, English has effectively attained a status of lingua franca in science, and the extent to which OA supports cross-language dissemination and consumption of information beyond English is not well understood. This preliminary work investigates English-language sources as referenced in OA articles across 8 world languages in the four most common subject areas of study (i.e. Medicine; Biochemistry, Genetics, and Molecular Biology; Engineering; and Social Sciences) as indexed in Scopus in 2023. Non-English languages and language families analyzed (i.e., Chinese, Spanish, Portuguese, and Russian) consistently included references to English language sources. Further, English-language articles strongly favored providing references to other English-language sources, with 17 out of 20 English-language articles exclusively containing English-language references. Future work will extend the current investigation, while considering the affordances and disadvantages of Scopus to access non-English OA articles.

In this paper, we use a critical informatics approach to investigate institutional disinformation around 2022-23 labor organizing at three higher education institutions: Rutgers University, Temple University, and the University of California. Our contribution to the study of disinformation is the application of critical informatics perspectives that attend to structural power dynamics of disinformation within an institutional context. Understanding the political economic dynamics of disinformation and how these dynamics function can help more solidly contextualize and clarify how and why disinformation exists across different information systems, so that solutions to this social and institutional problem of disinformation can be more appropriately addressed and understood. The study describes disinformation tactics employed by institutional leaders during higher education labor organizing including: non-performative commitments to “community”, legal threats, misleading victories, and elite capture.

Worldwide events, including the war in Ukraine and the conflict in Gaza, have highlighted the effective use of social media to voice concerns about certain issues and create awareness. However, the negative effect of media is largely seen as a fertile ground for spreading misinformation. Misinformation is not a new concept. However, the spread by which information is spread and magnified has exponentially increased with the widespread use of media. Efforts to regulate media and control the widespread spread of misinformation are still lacking, and the final decisions are left to social media companies to self-regulate and supervise content.Given complexity associated with identifying misinformation using automated methods due to the subjective nature of information, it is more practical to focus on social noise as factor in spreading and magnifying misinformation on media.In this paper, we investigate methods of quantifying social noise using entropy and topic modeling. Results have shown a direct relationship between social noise and social entropy as a measure of uncertainty. Results have shown that social noise and uncertainty decrease with the use of URLs and rich content. This approach does not eliminate social noise and misinformation.identification and reduction of social noise can minimize the negative effect misinformation.

This paper discusses the ethical implications of citing retracted biomedical literature, particularly in the context of spreading misinformation within scholarly discourse. It examines the responsibility of scientists to combat disinformation and uphold ethical standards in their research practices. To guide our discussion, we studied citations of the most often cited retracted works containing disinformation in Web of Science. Our findings confirm prior research and demonstrate that most citations to retracted papers reference them to bolster arguments or use methodologies without acknowledging their status. We conclude by interpreting our findings through a framework of moral obligation and argue that scientists have a special responsibility to combat disinformation, which may harm others. Cognitive authorities, namely scientists, citing invalid publications may perpetuate false beliefs and erode trust in scientific integrity.

ChatGPT has shown promise in assisting qualitative researchers with coding. Previous efforts have primarily focused on datasets derived from interviews and observations, leaving document analysis, another crucial data source, relatively unexplored. In this project, we address the rapidly emerging topic of disinformation regulatory policy as a pilot to investigate ChatGPT's potential for document analysis. We adapt our existing qualitative research framework, which identifies five key components of disinformation policy: context, actors, issue, instrument, and channel, to sketch out policy documents. We then designed a two-stage experiment employing a multi-layer workflow using a dataset with highly relevant policy documents from US federal government departments. Through iteratively developing and refining six different prompt strategies, we identified an effective few-shot learning strategy that achieved 72.0% accuracy and a 70.8% F-score with the optimal prompt. Our experimental process and outcomes explore the feasibility of using ChatGPT to support manual coding for policy documents and suggest a coding approach for conducting explicit document analysis through an interactive process between researchers and ChatGPT. Furthermore, our results initiate a wider debate on how to integrate human logic with ChatGPT logic, along with the evolving relationship between researchers and AI tools.

Over the last two decades, medical libraries have shifted from being careful suppliers of health information for consumers and patients to taking on prominent roles in outreach initiatives. The emerging livestreaming technology provides opportunities for medical libraries to disseminate health information and facilitate dialogues between health professionals and the public. This article presents a case study of Fudan University Medical Library's innovative approach to health information outreach through livestreaming. By leveraging the expertise of health professionals and embracing the livestreaming technologies, the library successfully engaged a broad audience, surpassing the limitations of traditional outreach methods. Based on the case, the article discusses the transformative potential of livestreaming in reshaping health information outreach and promoting health.

Young adults are one of the most vulnerable populations susceptible to mental health concerns, yet they face various barriers in accessing resources and obtaining necessary support and treatment. While prior research has predominantly concentrated on technology utilization in mental health help-seeking, a more comprehensive understanding of how individuals navigate the broader landscape of available resources is crucially needed. To address this gap, we conducted in-depth interviews with 18 young adults to explore their lived experiences and resource navigation during the mental health help-seeking process. Guided by the Social-Ecological Framework, our study categorized the resources utilized by young adults into four categories: technological, interpersonal, community-based, and societal resources. Through thematic analysis, we report preliminary findings that identify specific characteristics of resources that young adults value and prioritize throughout their help-seeking journey. These findings provide implications for designing multi-level mental health resources and interventions to facilitate young adults’ help-seeking. We also discussed methodological considerations to encourage in-depth conversation with marginalized groups on sensitive topics like mental health.

This paper reports on findings from 31 semi-structured interviews with North American Information and Communication Technologies (ICTs)-based content creators who focus on LGBTQIA+ history. While the research broadly explored the information practices and digital preservation strategies of these content creators, this paper highlights how the participants navigated their respective ICT choices (ranging from personal blogs to Instagram accounts) and what affordances and constraints emerged from these choices. The paper highlights how such potentialities and pitfalls across varied social media and web-based platforms informed the sociotechnical practices of queer historical knowledge production and how the participants leveraged the features of various ICTs to expand the visibility of queer-affirming content and resources. The paper concludes by exploring these practices and their theoretical and practical implications for practitioners at the intersection of digital curation and archiving marginalized histories with an explicit emphasis on collaborative rather than extractive opportunities for collaboration between such content creators and cultural heritage institutions

The COVID-19 pandemic has highlighted the importance of crisis research, particularly in the response phase. This research explores long duration crisis sensemaking of an Expert Advisory Group (EAG) during an emerging disease outbreak which has implications for the conduct of COVID-19 public inquiries. The members of the Ontario SARS Scientific Advisory Committee (OSSAC) for the 2003 SARS outbreak in Canada, provide the context for this study. Among their duties, these experts were tasked to write directives (mandated protocols) that govern all aspects of hospital life, such as the protocol for transferring SARS patients. Data were collected in multiple forms, including: public inquiry reports, meeting minutes, newspaper articles, and interviews. Following a constructivist grounded theory strategy, several iterations of data collection and analysis were completed. The findings include a conceptual framework that depicts the sensemaking process and illuminates the relationship between retrospective (after an event has occurred) and prospective (future-oriented) sensemaking.

While searching within digital humanities collections is an important aspect of digital humanities research, the search features provided are usually more suited to lookup search than exploratory search. This limits the ability of digital humanities scholars to undertake complex search scenarios. Drawing upon recent studies on supporting exploratory search in academic digital libraries, we implemented two visual keyword/result linking approaches for searching within the Europeana collection; one that keeps the keywords linked to the search results and another that aggregates the keywords over the search result set. Using a controlled laboratory study, we assessed these approaches in comparison to the existing Europeana search mechanisms. We found that both visual keyword/result linking approaches were improvements over the baseline, with some differences between the new approaches that were dependent on the stage of the exploratory search process. This work illustrates the value of providing advanced search functionality within digital humanities collections to support exploratory search processes, and the need for further design and study of digital humanities search tools that support complex search scenarios.

Prompted by recent innovations, artificial intelligence (AI) is increasingly being discussed across the museum sector regarding its implications for institutional roles and practices. However, AI in particular, is an ambiguous term, a ‘black box’ which is capable of containing and reflecting numerous values and ideals (Crawford, 2021). This paper positions discourse around AI as a ‘discursive’ infrastructure, capable of not only embodying ideals but also shaping and justifying certain institutional practices and roles. This paper thematically analyses 115 pieces of grey literature produced and shared by professional governance bodies in the museum sector from 1995-2023, mainly across Canada, the United Kingdom, and the United States. In doing so, it identifies four preliminary themes encompassing shifts in discourse over time which give shape to a contemporary discursive infrastructure. This prompts timely critical reflections of museum professionals and stakeholders on both imagined and overlooked public roles, responsibilities, and practices of the museum in relation to AI.

Digital video collections of dance, theatre, and other performances can provide unprecedented access to these ephemeral art forms. Making these collections searchable requires understanding of users’ needs, necessitating collaboration among subject matter, metadata, and information behavior experts. Focusing on the example of Circus Oz, a prominent Australian circus company, this paper describes an exploratory, user-centered process that draws on qualitative thematic coding techniques to create descriptive metadata schema. This iterative process illustrates the extensive labor involved, and the combination of expertise required, to create schema that extend beyond technical and basic categories to support rich search and discovery of cultural data collections.

In the past two decades, the digitization of humanities resources has rapidly expanded, giving rise to the field of Digital Humanities (DH). This evolution has fostered a growing need for effective interoperability—the ability to access and integrate data across diverse digital collections, tools, and platforms. Despite significant technological advancements aimed at facilitating interoperability, challenges persist. These challenges are not only technical but are also influenced by sociocultural factors, which include organizational and cultural elements. This paper aims to bridge the gap in the literature concerning these sociotechnical factors affecting interoperability within DH projects and collections.

This paper presents the analysis of the real-world experiences of DH researchers attempting to connect material from different sources. Each participant described a specific incident where combining software or content was particularly problematic. This data was analyzed via thematic analysis. The findings reveal a complex landscape of interoperability challenges, underpinned by a range of sociotechnical factors. These factors include, but are not limited to, discrepancies in metadata standards, differences in organizational cultures, and varying levels of technical expertise among practitioners. By highlighting these factors, the paper contributes valuable insights into the multifaceted nature of interoperability issues in DH, offering evidence-based plans to address these challenges.

This 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.

Human trafficking is a complex global issue that continues to challenge efforts aimed at raising awareness and preventing its occurrence. Overall, the literature suggests that social networking platforms have the potential to significantly enhance human trafficking awareness efforts but require careful strategy development to overcome algorithmic limitations and ensure meaningful engagement with users. Further research is needed to evaluate the long-term impact of social networking campaigns on human trafficking awareness and prevention. This poster is an extension of a presentation at the ASIS&T Midwest Chapter Spring 2024 Symposium with the purpose of increasing attention to this important research area. This poster provides five broad research questions that can be immediately addressed by information science researchers for the purpose of increasing understanding of this devastating global issue.

This study investigates disparities in telemedicine access and use among racial groups in the United States during the COVID-19 pandemic. Our analysis of a dataset from the National Center for Health Statistics (NCHS) reveals that telemedicine access and use varied significantly across racial groups, which the NCHS categorized as “Black,” “Hispanic,” “White,” or “Other.” Hispanic respondents were more likely to lack a regular place of care, which might explain their lower access to telemedicine, relative to other groups. White respondents reported relatively high telemedicine access. Black respondents also reported relatively high rates of telemedicine use, contrary to the literature suggesting lower healthcare access among African Americans. Findings underscore the need for public health agencies to disseminate information about telemedicine to all community members. We suggest future research exploring the socioeconomic barriers impacting individuals’ ability to access and use telemedicine.

Text-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.

While participating in the information search process, students are instructed to choose credible sources for their research. They often interpret this imperative as simply avoiding bias in the sources they choose. Unfortunately, students misunderstand bias and fail to recognize its significance in the scholarly conversation. One way librarians can help with this misunderstanding is by engaging with theoretical concepts such as positionality theory. Positionality theory offers librarians a way to examine bias’s function in the research process in order to teach students about its complexity.

While bias and positionality are concepts that have been explored in information literacy research, most research does not address them as related topics. This poster explores notions of bias, positionality theory, and strategies to employ that will help librarians use this theory to address student misunderstandings around questions of bias and credibility. This new understanding helps students determine their own positionality when approaching information searching.

In this paper we examine the determinants (trust and social influence) that influence users’ future use intentions of generative AI applications among black, indigenous and other people of color (BIPOC) users of generative AI applications such as ChatGPT and other large language models. An online survey instrument was administered to 119 BIPOC identified individuals residing in the United States. Descriptive and inferential statistics were used to analyze data through SPSS. Results indicate no statistically significant differences among ethnic/ racial groups in social influence and continued use intentions. However, with respect to trust, Hispanic/LatinX users perceive information from AI chatbots as more trustworthy and accurate, and that their search activity was more secure than other BIPOC populations. The study also found that trust and social influence were significant factors driving future use intentions.

People 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.

As part of a larger project to develop a scale for measuring web credibility perceptions, this poster reports on preliminary findings of a literature analysis to identify reflective and formative indicators of information credibility on social media. Of 90 papers found by a systematic database search and screening, this poster examines 20 papers focused on information credibility on microblogs (e.g., Twitter, Weibo) as a popular source for online users’ information seeking. Our analysis identified 22 reflective indicators (e.g., fair, accurate) and 31 formative indicators (e.g., reputable, attractive), eight (e.g., trustworthy, unbiased) of which overlapped. Given the intertwined, often blurred border between these two sets of items, the coding scheme developed in the present study can help determine the relevance and type of the items. Future research directions are discussed.

Affordances, described as action opportunities emerging from the relationship between a technology and its user, are a popular framework for understanding how people engage with social media platforms. Scholars have proposed this framework as a tool for performing critical analyses of technology, arguing that drilling down on affordances can help us understand how power shapes technology use. This conceptual analysis builds on the affordance mechanisms and conditions framework by Davis. Davis proposed to think of affordances in terms of mechanisms (request, demand, encourage, discourage, refuse, allow) and conditions (perception, dexterity, and cultural and institutional legitimacy). This framework sheds light on to whom and under what circumstances technologies afford certain actions to its users. I apply this framework to social media and provide examples that illustrate the framework’s components. For each component, I include a set of suggested questions that a researcher interested in a critical social justice-oriented analysis of social media affordances might ask. Asking these questions, I argue, can support a researcher in dissecting the biases, stigmata, and discrimination embedded in social media.

This study explores the effects of diversity, equity, and inclusion work on retention and job satisfaction of librarians. Using existing data from the Workforce Issues in Library and Information Science 2 project, a moderated mediation SEM model showed that being prepared to advocate for or serve diverse populations did not directly affect librarian retention, nor did collaborating with the community or promoting outreach and engagement with the community. However, this preparation and these job activities did affect librarians’ job satisfaction, which ultimately affected their decision to stay in the profession. In addition, preparation for DEI work and opportunities to work with the community are just as important for White librarians as for Black, Indigenous, and People of Color (BIPOC) librarians. These findings have implications for library and information science education, practice, and research.

This paper introduces TORCHLITE, an innovative HathiTrust Research Center (HTRC) open analytical and computational framework designed to offer efficient, open, and approachable access to the HTRC Extracted Features (EF) dataset via a well-documented web-based API. This poster will summarize project goals and progress, and discuss community engagement, which has played a pivotal role in this project. A hackathon event held in spring 2024, TORCHLITE fostered collaboration among digital humanities and information science scholars to develop widgets and notebooks utilizing the EF API. Through the hackathon, participants explored the API's capabilities, leading to the creation of over a dozen analytical widgets and interactive programming notebooks (e.g., Jupyter).

Library and information science has led data curation research and education for the last two decades, providing data curation education, professional development programs, and a robust professional opportunity. To keep current with the latest trends in competencies and tools needed to conduct data curation activities, this research conducted a systematic literature review of literature that captures competencies and tools to build a framework of current trends in data curation work that educators can utilize to ensure up-to-date educational materials for the next generation of data curation professionals. This poster presents the preliminary findings of this data curation competencies and tools analysis.

As AI-enhanced academic search systems become increasingly popular among researchers, investigating their AI transparency is crucial to ensure trust in the search outcomes, as well as the reliability and integrity of scholarly work. This study employs a qualitative content analysis approach to examine the websites of a sample of 10 AI-enhanced academic search systems identified through university library guides. The assessed level of transparency varies across these systems: five provide detailed information about their mechanisms, three offer partial information, and two provide little to no information. These findings indicate that the academic community is recommending and using tools with opaque functionalities, raising concerns about research integrity, including issues of reproducibility and researcher responsibility.

Many scholars have tried to investigate the identification, characterization, dissemination, and prevention of misinformation in recent years. But a fundamental question that lies behind these investigations is ‘Why do people believe in a piece of information, whether true or false?’. The primary objective of this study is to understand the psychological drivers of belief systems that makes individuals believe any information prior assessing its veracity. The study specifically tries to understand cognitive biases that influence an individual’s decision making about information in digital settings. Based on a quantitative survey with 41 participants, we try to induce cognitivity among the participants and try to measure the effect in their decision making. We find a major portion of our participants being cognitively induced which in turn had a significant effect on their decision making while engaging with information. Furthermore, we try to assess whether an individual’s political affiliation has any effects in perceived truthfulness while engaging with political information. This study shows us how easily cognitive bias can be induced and how it affects an individual’s belief structure in digital platforms.

This study aimed to construct a model of memory institutions (libraries, archives, and museums) as a lens for analyzing contemporary collective memory practices related to the Ainu people. First, we developed a pilot model to organize the current phenomena and practices of collective memories and suggest a respective circulation mechanism consisting of three core elements: media, collective representations, and collective memory. Additionally, employing this pilot model, we conducted close readings of 449 pieces of literature and illustrated the characteristics of collective memory practices and phenomena in a case study of the Ainu. Our findings indicate that 1) the traditional collective memory of the Ainu has gradually shifted toward recognizing them as a unique ethnic group, and 2) by translating their oral literature into diverse media, the Ainu have reshaped their cultural identity.

This paper deconstructs the profound context of Qi Culture from the perspective of Meme and constructs a structured and systematic knowledge description framework. It is useful for exploring the core values and promoting the creative transformation and innovative development of Qi Culture. The research process involves collecting user discussions and research data about Qi Culture from multiple sources, and then using the BERTopic model and qualitative analysis methods to obtain diverse examples of Qi cultural elements. Afterwards, from the Meme perspective, Qi cultural memes are extracted to construct a domain ontology, thereby achieving multi-dimensional semantic knowledge description of Qi Culture. This study helps to reveal the core elements of culture and their relationships, and will provide strong theoretical support for the integration and utilization of cultural resources.

The 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 during the so-called Web 2.0 era. I show how search and social media platforms disrupted publishers’ relationships with audiences and advertisers by appropriating control over news distribution and revenue. Using Anthony Giddens’ structuration theory, I explore the role of the platforms in restructuring the practices of newsrooms and undermining the values of journalism. I present a research framework for assessing symbiotic and parasitic relationships in sociotechnical systems through a mix of quantitative and qualitative methods. I use Bruno Latour’s actor-network theory to identify technological power in relationships between news publishers and Big Tech, and to suggest where publishers still have agency to begin resolving the crisis.

The launch of ChatGPT and other Generative Artificial Intelligence (GenAI) models has significantly influenced people’s perceptions of AI, making its impact a widely discussed topic in various areas. In Library and Information Science (LIS), researchers experience the opportunities and challenges presented by GenAI, such as using the rich text from books and digital collections as training data for GenAI and enabling quick summarization and reference consultation for patrons. Meanwhile, concerns have arisen about the generation of fake information and nonexistent text by GenAI and their potential effects on users. This project explores the themes in public discussions regarding the impact of GenAI on libraries. To explore public opinions about the impact of GenAI in libraries, we collected over 50,000 posts from X (formerly Twitter) between November 1, 2022, and May 6, 2024. We manually sampled 1,000 posts focusing on GenAI in the context of libraries. We conducted content analysis to identify the key themes and relationships among these themes. This poster presents our initial findings on different themes and highlights the trends in public opinion. Our analysis provides a curated dataset for training APIs for future investigation.

Digital housekeeping (DH) is an emerging research topic focused on the labour and social implications of setting up and maintaining technology in the home. Although examined by multiple fields, the literature on DH has yet to be reviewed and its potential overlap and synergy with library and information science (LIS) have yet to be explored. We therefore conducted a tool-supported review of 26 scholarly publications on DH. Below we summarise the people- and relationship-focused sub-topics and findings of DH literature and explore promising directions for future research in LIS, particularly in information behaviour and personal information management. The poster visually maps the sub-topics and disciplinary connections.

When interacting with chatbots, questioning is the starting point of the conversations. A well-phrased question can elicit desired responses and enhance information-seeking efficiency. Specifically, consumers can use various linguistic cues (e.g., emotional expressiveness) to phrase questions and shape responses. However, research on how to effectively phrase questions for chatbots was limited. This study focuses on consumers' questions for chatbots, examining how various linguistic cues (e.g., emotional expressiveness) influence responses. Further, it compares responses generated by community users and chatbots. Preliminary results showed that askers’ self-disclosure attracted emotional support from chatbots. Chatbots demonstrated higher expertise in responses to high-specificity questions and produced more analytical and unified responses. In contrast, user-generated responses were perceived with higher authenticity. This research underscores the role of question cues prompting chatbot-generated responses and illustrates the strengths and limits of chatbot-generated responses. Theoretical and practical implications are discussed in the final section.

This poster presents a community-focused participatory design methodology as a basis for developing a digital storytelling system for the Inuvialuit Settlement Region in Canada’s Western Arctic. It provides an overview of the various stages of digital storytelling system prototype design informed by an Inuvialuit elder’s storytelling gathering and a community experience open house. Among the prototype redesign ideas are empathy maps we are using to allow us to connect stories with designs and community needs in mind.

Open government information systems offer great potential for advancing civic life and democracy, but they also reflect and reinforce the biases and systematic inequalities faced by members of socially marginalized groups. We present results from a critical data modeling project that uses a data quality framework to examine open datasets published by police departments in order to understand how data modeling choices shape the social impact of these datasets. Using an arrest record dataset published by the Los Angeles Police Department as a case study, we present we present results detailing the representation of racial data and the presence of children in the dataset. We argue that current data quality frameworks for open government data are insufficient for critical data studies due to an orientation around institutional and computational interests. Incorporating feminist data ethics into data quality analysis provides an approach to data quality that centers people and communities. We propose a definition for data quality of open government datasets based on an ethics of care that centers the needs of vulnerable populations and accountability of institutions toward their communities.

Research into multimodal model biases has largely concentrated on gender and racial issues, neglecting key areas such as biases related to religion, nationality, sexual orientation, and disabilities, as well as the subtleties of question intent and common biases. To address these gaps, we developed the Multimodal Bi-Stream Structure-based Model (MBSS) for Bias Mitigation, an innovative approach that focuses on these underrepresented groups by focusing on religion, nationality, sexual orientation, and disability but also to identify common biases and question intents. The MBSS employs a dual stream bias mitigation system, where the standard stream addresses common biases and the mitigation stream targets specific biases, allowing for nuanced and effective bias reduction by making decisions based on the difference in predicted probabilities between these two streams. Empirical results demonstrate MBSS's efficacy, especially in reducing religious bias by up to 71%, highlighting its potential to foster a more inclusive digital environment and enhance the representation of diverse groups.

Although the Social Q&A community has gained increasing popularity in solving issues in various fields, how users perceive the information credibility on this platform remains inconclusive. This paper developed the model to discuss the relationship between linguistic cues/perceived reputation and information credibility. Methodologically, the paper used an online survey to collect 759 units of valid data and test the model through the SEM technique. The results showed that perceived specificity, logic, relevance, and reputation are positively related to information credibility, while perceived expressed emotion showed a negative association. These findings help to inspire answer contributors to employ strategies to make their answers more credible.

This poster reports developing dissertation research, exploring the individual and collective storytelling practices of people experiencing difficult to diagnose chronic pain and fatigue (CSSs) on TikTok to understand and ultimately support these embodied, creative, and collective storytelling abilities on and off-app. Ongoing data collection and analysis using constant comparison, guided by critical disability studies sensitizing concepts, of 150 videos and their top-20 comments, and interviews with 6 community members indicate that people with CSSs are using TikTok affordances to tell and scaffold complex micro-stories sharing disabled expertise; creatively visualize invisible illness; use intimate, documentary cinematography; tell composite stories using skits and collapsed time; and make audio, visual, and community/collective mimetic alterations. This poster shares a pragmatic sampling approach using algorithm training, and results from visualization activities during interviews which indicate a messy overlapping landscape of what I call algorithmically mediated online health communit(ies) (AMOHCs), which have slippery boundaries, where health information is found serendipitously, and people connect across specific chronic illness categories. This dissertation-in-progress is developing a set of disability-specific storytelling abilities, which interview participants indicate are transferable to off-app settings; and upcoming codesign workshops creating storytelling support materials with community members will further distill disability storytelling support needs.

This poster describes research considering how public library staff manage conflicting internal and external information about their identity. 21 public library staff members completed audio diaries and sat for an interview 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.

As competition in technologies such as semiconductors and AI intensifies, the importance of technology analysis in establishing R&D strategies is increasing. Although frequently used, technological document searches by domain experts often suffer from limitations in qualitative judgment and inconsistency. This study proposes a process for quantitatively assembling a collection of documents on specific technologies using an academic database and a language model. Using approximately 92 million records from the Web of Science, we identified related keywords from author keywords and used scientometric approaches to determine a core set of technology-related literature. Then, a deep learning-based Sentence Transformer model was employed to extract technology literature with high similarity to the documents, ultimately forming a set of documents for technology analysis. This study aims to overcome cognitive limitations and introduce quantitative criteria for technology analysis, which is particularly significant for data-driven science and technology policy.

Universities now focus on the societal impact of research, yet there is a disconnect between the information environment and the needs of academics undertaking impact-related work. While universities acknowledge that undertaking research designed for societal impact requires changes to research culture and practices, the information and support provided do not reflect the complexity of that work. Professional staff mediate information from various sources, such as government policies, university strategies, funders’ requirements, and academics’ needs; however, the effectiveness and appropriateness of support provided is understudied. This qualitative study explores the information behaviors of professional staff developing and facilitating academics’ engagement in impact work. It finds that while professional staff undertook iterative processes of self-informing, mediation, and information use to support impact work, their privileging of institutional and external influences, ahead of academics’ needs, creates a disconnect between services provided and what academics need. This study increases awareness of the influence of professional staff’s information behaviors on impact supports, which can inform academic-centered, responsive approaches to support contextualized, personalized and responsive information services in universities.

The development of cultural and creative products by libraries helps to promote the creative transformation and innovative development of culture, and meets the demand for high-quality cultural consumption. Based on the formation mechanism of library cultural and creative product consumption behavior, this study takes China as the research object and proposes that libraries should pay attention to the evolution of the consumption theme, meet the symbolic consumption demand, create new consumption scenarios, pay attention to product quality improvement, and improve the awareness of consumption service. The results of the research show that in the product dimension, the richness of categories is emphasized, and the aesthetic attributes of products are emphasized. In the demand dimension, it focuses on commemorative value and social value, and satisfies both emotional value and cognitive value. In terms of channel dimension, it covers both online and offline channels, and reasonably utilizes the KOL (Key Opinion Leader) effect. In the price dimension, we refuse to spend a high amount of money, but are willing to pay for quality and originality. In the service dimension, focusing on the purchase experience, the service quality affects the subsequent consumption.

Hundreds of Libraries, Archives, and Museums (LAMs) around the world run crowdsourced transcription projects in order to engage users with their collections. Some LAMs explicitly use crowdsourcing projects to make non-machine-readable images of documents, such as manuscripts, discoverable to people who are blind or have low vision. We present findings from Crowdsourced Data: Accuracy, Accessibility, Authority (CDAAA), a 3-year Institute of Museum and Library Services (IMLS) grant project that investigates whether and how LAMs integrate crowdsourced transcriptions into their discovery systems, and whether these efforts result in accessible web-content for blind people and those with low-vision who use assistive technology to navigate the web. We share research findings as well as practical suggestions for those in charge of crowdsourcing projects, the resulting transcription data, or similar web-based textual content such as scholarly editions. These research and practice-oriented findings are relevant to any national or local context where inaccessible images are transcribed, and are especially timely in the US context given recent Federal rule-making to ensure that all web and app-based content provided by US State and local governments is accessible, including tools, resources, and content created in-house, through contracts or by license (2024).

Young adults have been shown to prefer online sources when searching for information about sensitive topics, such as sexual health (Baker et al., 2020; Buhi et al, 2009). This is especially true for LGBTQ+ young adults, who may want to keep potentially sensitive information needs about their sexuality or gender identity a secret as they explore their identity, or because they may need to for safety reasons (Delmonaco & Haimson, 2022; Kitzie, 2019). This pilot survey study aimed to explore what types of information needs LGBTQ+ young adults may keep secret from the people in their lives, at what age they had their first secret information need, what skills helped them to fulfill their secret information needs, and which skills/resources they would have liked to have when trying to address their secret information needs. Preliminary results suggest that topics relating to health and participants’ LGBTQ+ identities were more likely to be kept secret than other everyday life information needs.

Cybersecurity is a perennial concern for technology companies, consumers, and policymakers. Recently it has become a lever of opposition against the Right to Repair movement as companies and industry groups position security and reparability as antagonistic, even incompatible, interests. This paper pushes back on those claims. We use critical discourse analysis to explore two contemporary controversies at the intersection of security and repair: (1) the U.S. National Highway Transportation Safety Administration's statements concerning automotive repair in Massachusetts and (2) the memorandum of understanding between John Deere and the American Farm Bureau Federation concerning agricultural equipment repair. We find that security concerns are raised as featureless specters of harm rarely supported by concrete or compelling evidence that reparability risks security. Rather, these arguments reflect a rhetorical strategy aimed at thwarting the repair activities of consumers and independent technicians, shifting ongoing policy debates, and influencing public sentiment around the right to repair.

This systematic literature review synthesizes published sources from the ASIS&T Digital Library and the ACM Digital Library to develop a definition of the carceral state and to show how the term has been used in contemporary technology-focused research. The carceral state concept has been adopted and applied widely in multiple areas of social scientific research to refer to the formal institutions of the criminal justice system proper and other social arrangements, ideologies, practices, and technologies that punish, surveil, and contain populations. Our review reveals a recent and increasing engagement with the carceral state in the collections surveyed. Encouraged by this increasing attention, this review is an attempt to introduce the carceral state as a guiding framework for tech-society research and to consider implications for advancing responsibility, reflexivity, and care in the creation and evaluation of information systems, programs, and services.

This research takes the accessibility of rural public digital cultural services as the research object. Based on the accessibility framework and system evaluation theory, an evaluation index system is constructed through investigation and analysis. Firstly, through on-site investigation and interviews, explore service accessibility factors and establish a feature factor model; Secondly, based on industry norms, an evaluation system was constructed that includes primary, secondary, and tertiary indicators, and was revised and validated through empirical analysis; Finally, the AHP- fuzzy comprehensive evaluation method was applied to study the indicator system. The results indicate that the accessibility of public digital cultural services is good, but still needs to be improved, and improvement path suggestions are proposed from the aspects of resource supply, services, organization and management, etc. The research enriches the evaluation system of public cultural services and provide reference for the government to improve public digital cultural services.

This exploratory study investigates the influence of stressful life events on privacy-protective behavior. We examine the effects of the ongoing state of war in Israel on the rate of deliberate falsification of personal details during website registration, in a user study of 107 students at Israeli universities. The students were queried using a quantitative method in online closed-ended questionnaires. This research builds on our previous work (Weinberger & Bouhnik, 2021) examining the effects of other stressful life events, specifically the COVID-19 pandemic, on the privacy-protective behavior of Israeli students. We found that distrust of website operators and the desire to remain anonymous are the most common reasons for identity falsification. In addition, our analysis showed that the greater the concern over privacy, the more likely identity falsification was to occur. The Israel-Hamas War in Israel seems to have exacerbated privacy concerns among online users, in turn increasing the tendency of users to exhibit privacy-protective behavior.

This study investigates the impact of ancient book image quality on the accuracy of cultural heritage crowdsourcing correction tasks. The study processed 27 pictures from an ancient book to adjust their quality in terms of clarity, seal coverage and background color. Based on human error theory, the study divided common errors made by participants in correction tasks into slips and mistakes, and analyzed the impact of different image qualities on these errors. The experiment was conducted using an online experiment platform, with 25 participants each completing 6 tasks related to clarity, seal coverage, and background color. The results show that the clarity of ancient book images significantly affects the number of rectifications and mistakes in the tasks. Different degrees of seal coverage significantly impact the number of rectifications and slips, while the background color of the images mainly affects the number of mistakes.

Preprints have played an unprecedented role in disseminating COVID-19-related science results to the public. The study aims to elucidate the role of preprints during the COVID-19 public health emergency (2020-2023) through a quantitative analysis of journal papers. Among the 247,854 COVID-19-related papers published in PubMed, 12,152 were initially released as preprints and were eventually published in 1,380 journals. This number is more than five times the 246 journals to which submissions can be made directly from bioRxiv through the B2J program. Journals with higher impact factors and Normalized Eigenfactor scores tend to publish a larger number of preprint-derived articles. The proportion of preprints among PubMed papers was 0.049, but this varies significantly by journal. In the top 30 journals, most exceed this proportion, indicating that these journals are preferred by authors for submitting their work. These findings highlight the growing acceptance and impact of preprints in the scientific community, particularly in high-impact journals.

This study explores the influence of Artificial Intelligence (AI) literacy on trust in AI across critical sectors, including transportation, healthcare, and social relationships. An online survey was conducted with 300 participants. The findings revealed that those with advanced AI literacy were more likely to report increased trust across all scenarios. However, those with intermediate AI literacy seemed to view AI with more skepticism, particularly in high-stakes contexts such as transportation and healthcare. This result indicates that AI literacy is crucial in shaping public attitudes toward AI technologies, 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 validate these findings and optimize educational initiatives for equitable AI integration.

Drawing on knowledge creation capability in the Science of Science, this paper aims to determine scientists` knowledge creation capability (KCC) and figure out whether there is any gender gap in that. We construct a career matrix to compute KCC of scientists in Economics, Astronomy, and Computer Science each year, from the perspective of knowledge source and diffusion. We find that male scientists` KCC is not significantly different from that of female scientists, and the two groups of scientists have the same KCC distribution. In the first 10 years of their academic careers, female scientists have higher KCC than male scientists but have lower KCC in the 15 to 40 years. After controlling a series of confounders, it`s found that female scientists have higher KCC than male scientists, even though there is not much of a difference in KCC. The results remain the same after we perform a robustness check.

Little is known about the differences between trolling by influencers and followers, the difference between proactive and reactive trolling, and the relationship between them. Based on a content analysis of 1,386 comments on 160 posts of 19 influencers during a Sina Weibo trolling event, we found that influencers troll more frequently than their followers and they troll proactively more often than reactively, while followers troll reactively more often than proactively. Influencers generated less but more impactful content compared with their followers. In both proactive and reactive trolling, influencers derailed the discussion while followers provoked through their proactive trolling and insulted through their reactive trolling. These findings extend the research scope of trolling asymmetry and establish theoretical connections between social media roles and proactive-reactive trolling.

It is of paramount importance to support midwifery practice with research evidence. Nevertheless, Canadian midwives encounter significant obstacles in accessing research evidence for practice. As part of a broader study which seeks to identify an optimal method for providing midwives in Canada with access to online clinical resources, this poster reports on the preliminary findings of the survey phase. The objective of this study was to ascertain the frequency with which Canadian midwives utilize information sources accessible to them through institutional affiliations and to determine whether they are aware of the financial cost of providing such services. Additionally, the survey sought to ascertain whether their perception of a resource being free or premium affects its perceived value and use intentions. A descriptive survey design employing a structured web-based questionnaire was used to collect the requisite data. The preliminary findings indicated that participants perceived the value of free sources to be lower than premium sources and that they intended to use premium sources more frequently if they were able to pay for them. Moreover, the study demonstrated that participants frequently seek information online to support clinical practice, yet they utilize access to online resources through their institutional affiliation less so.

A key challenge in conducting comparative analyses across social units, such as ethnicities, cultures, or religions, is that data on these units is often encoded in distinct and incompatible formats across diverse datasets. This can involve simple differences in the variables and values used to encode these units (e.g., Roman Catholic is V130 = 1 vs. Q98A = 2 in two different datasets) or differences in the resolutions at which units are encoded (Maya vs. Kaqchikel Maya). These disparate encodings can create substantial challenges for the efficiency and transparency of data syntheses across diverse datasets. We introduce a user-friendly set of tools to help users translate four kinds of categories (religion, ethnicity, language, and subdistrict) across multiple, external datasets. We outline the platform’s key functions and current progress, as well as long-range goals for the platform.

Archives can reinforce silences of marginalized communities, further erasing their stories and voices. This study aims to understand the contours of archival silences, focusing on experiences of Chinese people in the United States in the late 19th and early 20th centuries. We are building a digital archival collection that centers Chinese peoples’ voices around the Rock Springs Massacre of 1885, an incident of racial violence that killed at least 28 Chinese people and displaced hundreds more. As we selected items, we wrote structured reflections to critically examine each item’s content, source, presentation, arrangement and description in their original context, as well as any absences and silences that we encountered in the search and assessment process. Based on a praxis of radical empathy, ethics of care, and slow archives, we engaged with our positionalities, past experiences, professional training in Library and Information Sciences (LIS), and affective responses throughout the research process to reflect on how they shape our decisions and hesitations in item selection and contextualization. Three main themes emerged from a preliminary thematic analysis of our reflections: complexity of archival silences and silencing, challenges of inclusive representation, and problematizing and going beyond primary sources.

Critical Race Theory (CRT) has recently garnered considerable political attention. Given the increasing role of white identity in politics, CRT discourse provides an opportune moment to leverage white Americans’ growing grievances. This paper examined how partisan news headlines incorporated white in-group identity in the discourse surrounding CRT and how these narratives spread within partisan media ecosystems. We employed a critical disinformation approach and computational methods to analyze partisan news headlines (N = 7,320) shared between January 1, 2016, and December 31, 2022. To investigate how the narratives spread within partisan news media ecosystems, this study adopted granger causality tests as a method of time series analysis and the study results disclosed evidence of intra-lean influence among right-leaning sources.

Based on the Governing Knowledge Commons framework, this study examines what users do and think about managing misinformation issues in the Twitter community, and how these are affected by user characteristics. Undergraduates from a public university in the US participated in the study (n = 464) by completing an online survey. Findings shed light on how Twitter community members tackle misinformation issues and inform policies and system designs to support the members’ actions and preferences.

Licensing data is critical to promote research data sharing and reuse. There are many standard licenses, customized licenses, terms of use, and use agreements used in practice, which are heterogeneous and hard for data providers to choose. This study used a qualitatively-driven-mixed method, including stratified and systematic sampling methods, and Grounded Theory to explore data licensing of the Dataverse repositories. We focused on the data usage and created a list of categories of permitted and restricted data usage. The categories help data providers understand and choose appropriate licenses when sharing data. They also build a foundation for our future work to create a faceted knowledge structure for research data licensing.

This poster explores the impact of design in historical and cultural games, focusing specifically on JX3 Online, a Chinese 3D massively multiplayer online role-playing game. It investigates how these designs influence players’ perceived value, game satisfaction and game engagement. The study leverages data from an online survey of 300 respondents. It finds that both aesthetic and functional design elements, along with accurate historical representation, significantly enhance players’ perceived value. Additionally, a positive relationship exists between players’ perceived value, satisfaction, and engagement levels. This research underscores the potential of historical and cultural games for cultural education and dissemination. It emphasizes the need to blend engaging design with educational content effectively.

Remote work and its challenges have been around for decades, yet the COVID19 pandemic brought these challenges to the fore and prompted organisations to address them. One of the challenges is the potential loss of tacit knowledge (knowledge that resides in the heads of “knowers”) among knowledge workers by shifting from in-person to remote work. This paper reports on the initial findings of a pilot study that interviewed knowledge workers about the interruption of information sharing, the importance of technology to resemble in-person working, and organization adaptability. The findings highlight the importance of in-person interactions to exchanging tacit information but indicates that for organisations that are resilient, technology can mimic in-person sharing.

Small and Medium Enterprises in rural areas are likely to be impacted by the digital divide arising from their geographic location and size of their organizations. With the integration of AI in to many business processes and systems, it is important to investigate how AI will impact the digital divides, especially in light of its potential to minimalize this disadvantage. This paper presents the preliminary findings of a qualitative study investigating how SMEs in rural Norrland are impacted by AI.

The information behaviors of people who have COVID-19 have been well studied, however, there is a gap regarding those who experience long COVID. We conducted an exploratory mixed-methods study to examine the information needs and practices of these COVID ‘long-haulers’ with the intention of suggesting new health communication strategies to help people in similar health crises or future pandemics from an information standpoint. Several themes have emerged from our preliminary findings: how participants tend to define long COVID, their long COVID-related information seeking and their difficulty with finding such information, and their experiences with discrimination and stigma. Throughout data collection, we have experienced issues with suspicious respondents (such as potential bots), though we were able to implement new criteria to determine legitimacy. Data collection and analysis is still ongoing and we expect to report additional findings in the near future.

Toxic content on social media has posed a significant threat to user experience and societal stability. In contrast to explicitly toxic content, implicitly toxic content, lacking overt toxic words, is challenging to identify through single-text features. Therefore, this study proposes a multi-feature fusion algorithm to recognize implicit toxic content. Firstly, we collect various features for each post, including text content, likes, comments, user information and other features. Subsequently, employing a multi-head attention mechanism, we extract and fuse these features. Then, utilizing ensemble learning algorithms, we identify implicitly toxic content based on the fused features. Finally, we analyze the decision-making process of the model using an interpretable algorithm and derive the most critical features and factors for identifying implicitly toxic content. The experimental results show that the proposed algorithm outperforms algorithms such as BERT, TextCNN, and XGBoost, demonstrating the advantage of multiple features over a single text feature in recognizing implicitly toxic content. In addition, this study provides insights into the decision-making process of the model and provides more effective toxic content management strategies for social media platforms.

The rapid growth of artificial intelligence (AI) and machine learning (ML) has led to significant innovations but also raised ethical concerns. Researchers and students designed an ethical online game in this study to spread awareness about making informed decisions when using AI and ML. Conducted within a directed research group (DRG) curricular method, the study engages students as co-researchers to develop a game, from developing ideas to playtesting the game in a class setting. The study employs a quantitative methodology to analyze a survey that 32 students, each with diverse backgrounds and knowledge in game development, conducted after each class session over three semesters. Findings indicate that self-reported engagement changes depending on the activities done in each session, with students feeling capable of contributing to research and game design.

Towards the goal of constructing an agent that can assist users in completing complex tasks and searching for information from multiple sources. We propose a complex task-supporting application, SMART (Search Maps and Routes/Trails), which combines complex task modeling through users’ web activities and expands search support to include maps and routes/trails of both subtasks and sources within complex tasks through case-based reasoning. We also summarize the results of a user study conducted to evaluate the usability of the SMART prototype system. The experimental results show that SMART could help searchers make search strategies, select information sources effectively, increase confidence during task performance, and improve search results. This highlights the value of both types of maps and routes/trails of subtasks and sources in complex task-supporting.

Cataloging and metadata professionals in libraries have an interest in learning about the Semantic Web and Linked Data technologies. However, with limited release time and funding for continuing education, professional development opportunities need to be designed with learner needs in mind: free, flexible, and fun. Our regional Linked Data interest group approached this challenge by developing an annual program in the form of a five-day edit-a-thon, providing training and hands-on experience in creating and editing Wikidata. The flexible format makes use of synchronous video conferencing, detailed online documentation, independent editing days for hands-on learning, and opt-in community discussion via Slack. Individual and project progress was tracked with the Wikimedia Event Dashboard. This poster proposes the Wikidata edit-a-thon as an ideal method to fulfill a professional development need of library staff by introducing participants to the principles of Linked Data through this flexible and interactive event.

Undisclosed deviations between preregistration plans and published articles challenge research transparency. This study examines whether open science experts can aid in assessing such deviations by comparing their evaluations with those of psychology experts. Using content analysis, we compared assessments of 25 preregistration plans and articles, following a psychologist studying preregistration discrepancies framework. The 72.6% agreement rate suggests differences stemmed from distinct perspectives. Psychologists leveraged domain knowledge to scrutinize subtle inconsistencies, while open science experts, deeply committed to transparency principles, focused more on procedural adherence. Open science experts, unfamiliar with psychology's implicit norms, faced challenges in discerning field-specific knowledge and interpreting ambiguities as non-deviations due to unclear preregistration formats and ambiguous content. Targeted support and collaborative approaches engaging both expert groups could enhance preregistration adherence evaluations, balancing the open science experts' emphasis on transparent processes with the psychologists' domain expertise, ultimately strengthening research transparency and reproducibility.

Research teams from the industry, especially big technology companies, have been pushing impactful research work in the field of artificial intelligence (AI), changing the prospects of the field and the careers of many researchers. Research teams from big technology companies usually possess more data, bigger computing infrastructure, and research talent, granting them the advantages in advancing AI research. While most previous work focuses on investigating the advantages the industry has in the field of AI, and how their research publication is different from those published by academic teams, few research has been done to investigate whether working as an industry researcher is beneficial at the individual level. In this work, by analyzing co-authorship networks of researchers published in AI conferences, we investigate whether working in the industry gives researchers advantages in “intangible” forms, such as social capital, represented by the collaborative relationships they gained or maintained. Our result shows that the many advantages industry researchers possess correlate with the social capital they have, measured by degree centrality, eigenvector centrality, betweenness centrality, and effective size.

This paper describes how we developed and applied an exploratory critical friends orientation to a critical literature review that explores how critical feminist theories and approaches have been used in archival studies literature and reports on insights generated by this method. A critical friend is a trusted ally and critic who both values our ideas and can push them forward. Our “critical friends” critical literature review includes two parts; using traditional critical review methods we identify and synthesize how critical feminist approaches have been employed in archival studies literature. Atypically, and in part two, we also pay attention to those scholarly articles that discussed relevant or related concepts but were ultimately excluded from our final literature review corpus during the appraisal process. These peripheral articles act as critical friends to the research area under review. We describe how this approach identifies disciplinary boundaries and traditions and explores areas of overlap across intersecting and adjacent fields. A critical friends approach allows us to generously interpret and analyze the complex concepts of “feminisms” and “archives” across disciplinary fields in order to identify, learn from, and engage across fields that have much in common as well as fundamental differences.

This study uses the concepts of information practices and care as theoretical lenses to explore the information practices of casual academic staff, who experience complex information contexts. Semi-structured online interviews followed by thematic qualitative data analysis revealed that, while these academics care deeply about their contributions to student learning, they experience a lack of institutional support and care. This experience leads to individualized information practices, and dependence on an uncertain and unreliable information landscape, characterized by information marginalization, information precarity, and information vulnerability. Exposing the informational challenges experienced by this large subpopulation of academic laborers can support efforts to improve their information environments and support their work. The study also contributes to contemporary theoretical understandings of information vulnerability within academic contexts.