bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessing Gender and Racial Bias in Large Language Model‐Powered Virtual Reference
Authors: Liu Jieli , Wang Haining
Published: October 2024

To examine whether integrating large language models (LLMs) into library reference services can provide equitable services to users regardless of gender and race, we simulated interactions using names indicative of gender and race to evaluate biases across three different sizes of the Llama 2 model. Tentative results indicated that gender test accuracy (54.9%) and racial bias test accuracy (28.5%) are approximately at chance level, suggesting LLM‐powered reference services can provide equitable services. However, word frequency analysis showed some slight differences in language use across gender and race groups. Model size analysis showed that biases did not decrease as model size increased. These tentative results highlight a positive outlook on integrating LLMs into reference services, while underscoring the need for cautious AI integration and ongoing bias monitoring.
Divide & Conquer: Critical Informatics Approaches to Disinformation
Authors: May Emma , Paris Britt
Published: October 2024

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.
Coconut Libtool: Bridging Textual Analysis Gaps for Non‐Programmers
Authors: Santosa Faizhal Arif , Lamba Manika , George Crissandra , Downie J. Stephen
Published: October 2024

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.
Exploring the Opinion Shifting of Social Media Users Based on Stance Detection
Authors: Sun Ran , An Lu , Li Gang
Published: October 2024

Fine‐grained mining of stance change among social media users during crises contributes significantly to a comprehensive understanding of the development of public opinion online. This study collected detailed information on 227,281 Twitter users who continuously participated in the discussion about the COVID‐19 vaccine to analyze patterns of opinion shifting. Utilizing the COVID‐Twitter‐BERT pre‐trained model, we detect each user's stance on the COVID‐19 vaccine and construct a time‐series dataset reflecting their stance over time. A prediction model for users' opinion shifting is established based on the LightGBM model, and the SHAP explanation method is used to rank the importance of features to identify key features influencing the opinion shifting prediction. Our research findings indicate that nearly half of the users maintained a consistent stance on vaccines, with a relatively low proportion of shifts between pro‐vaccine and anti‐vaccine stances. The F1 value of the prediction model based on the LightGBM model is 0.92. These findings can assist in monitoring social media user opinions online.
Transitioning to Human‐Centered AI: A Systematic Review of Theories, Scenarios, and Hypotheses in Human‐AI Interactions
Authors: Wang Di , Zheng Kaiyang , Li Chuanni , Guo Jianting
Published: October 2024

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.
Deceiving Dichotomies: On the Interrelation of the Archive and the Cultural Memory Canon
Authors: Youngman Tyler , Modrow Sebastian , Meth Isaac
Published: October 2024

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).
Will Users Prefer Cultural Heritage Exhibition in Digital Twin? The Mediating Role of Perceived Authenticity under the Moderation of Individual Profiles
Authors: Zhao Liang , Wang Lu , Yu Feng , Zhao Yijun , Wang Yujue
Published: October 2024

Digital twin techniques provide unprecedented opportunities in cultural heritage and museum exhibition services. As the techniques intersect with cultural heritage, a compelling question arises: whether users accept the new service with pleasure or show aversion to the technology. In this paper, we compare user attitudes towards digital twin exhibition and traditional physical exhibition through laboratory experiments. Drawing from the Stimulus‐Organism‐Response (SOR) theoretical framework, we incorporate perceived authenticity and individual profiles such as field experience and personality traits to explore the underlying mechanisms and boundary conditions influencing user attitudes towards digital twin exhibitions. It showed that compared with traditional exhibition, users with low level of cultural heritage familiarity, agreeableness trait and openness trait would perceive less authenticity in digital twin exhibition, and in turn report more negative attitudes towards the exhibition. The findings contribute to a better understanding of the fusion of technology and cultural heritage from user's perspective and provide insights into technology‐empowered services of cultural heritage and smart museums.
Immersive Technology in Education
Authors: Bosman Isak de Villiers , Smith Annique Elizabeth , Wong Yan Lau , Ka Kwan Sui Dave , Alemneh Daniel , Chow Anthony
Published: October 2024

Immersive technologies like virtual reality (VR) and augmented reality (AR) are transforming higher education by providing engaging, experiential learning opportunities. However, their integration into educational practices presents challenges, including high costs, technological complexity, and the digital divide. This panel explores the application of immersive technologies in higher education, focusing on developing an immersive technology lab designed to support their adoption across the institution. The discussion will cover global perspectives on immersive learning, showcasing varied adoption rates and strategies across different regions. Additionally, the panel will highlight research opportunities in immersive education, emphasizing the need for studies on effective integration, impact on learning outcomes, and strategies to overcome barriers to adoption. The goal is to identify sustainable approaches to expose staff and students to immersive technologies, ensuring equitable opportunities for all stakeholders.
Creative Research Engagement from Design through Application to Dissemination: Maximizing the Research Impact through Creativity
Authors: Budd John M , Senteio Charles , Nesset Valerie , Mansourian Yazdan ,   Members of the ASIS&T Research Engagement Committee
Published: October 2024

The value of research is often determined by its impact and ability to catalyze positive change in the life of communities, technological developments, and the advancement of education. However, translating research findings and innovative conceptualizations into practical applications is not always easy for various reasons. As a result, there may be difficulty achieving a buy‐in from community and industry partners who are essential for elevating innovative ideas to the level of working models and tangible outcomes. Similarly, it is not always immediately clear how research findings should be integrated in teaching and learning to improve the relevance of education to community and industry needs. In many situations, creativity and imagination are key to transforming research into impactful applications. Can we reimagine partnerships for the benefit of praxis? Can we design unique methods of research dissemination that will reach and engage a broader community, beyond a specialist circle? Can we present ideas and concepts to the world in a way that excites and motivates people, moving them to action? The presenters will provide examples of work that addresses these questions and will work with the audience to help with guidance in their own work.
Accessibility for Motor Disabilities in Immersive Technologies
Authors: Holmner Marlene , Meyer Anika , Ka Kwan Sui Dave , Bothma Theo , Alemneh Daniel , MacLeod John
Published: October 2024

The rise of immersive technologies such as virtual reality (VR), augmented reality (AR), and extended reality (XR) has revolutionized experiences in industry, entertainment, and academia. However, individuals with motor disabilities face challenges in accessing these technologies due to conventional interaction methods relying on motion‐tracked controllers. This panel discussion explores the barriers faced by people with motor disabilities and the potential benefits of overcoming these challenges. It delves into innovative interaction methods, the role of immersive technologies in physical rehabilitation, and designing for diversity and inclusion. Ethical considerations in creating accessible immersive experiences, such as privacy, equity, and responsible data use, are also addressed. Future directions for accessible immersive technologies are discussed, highlighting advancements in adaptive equipment and interfaces. The panel aims to foster collaboration between technology developers, researchers, and disability advocates to ensure immersive technologies are inclusive and beneficial for all.
Researching Informational Technologies of Trust: From Blockchain to Paradata and Digital Archives
Authors: Huvila Isto , Lemieux Victoria L. , Sköld Olle , Donaldson Devan Ray
Published: October 2024

Information science research has a long‐time interest in diverse informational means of codifying, communicating and maintaining trust as an antecedent and outcome of information, and a central constituent of information practices. While the landscape of the gatekeepers and guarantors of access to trustworthy information for a long time remained relatively stable, since the turn of the millennium, it has evolved rapidly with several new and repurposed social and digital technologies developed and repurposed for mediating and preserving trust. The purpose of this panel is to engage in exploring from theoretical, practical and empirical perspectives such technologies across the information field. The panelists present research conceptualizing, documenting, developing and describing informational technologies of mediating and preserving trust specifically addressing: 1) what different approaches to mediating and maintaining trust exist and can be identified in information science and technology research and practice; 2) how different information technologies of trust influence the understanding and conceptualisations of trust and trustworthy information; and 3) what novel insights from the current state‐of‐the‐art research can be drawn for helping practitioners to empower and put people first when interacting with informational technologies of trust in different areas of the information field, including knowledge organisation and information behaviour and practices, records management, digital preservation, and and development of information systems and services.
Queer Data
Authors: Keilty Patrick , Cifor Marika , Watson B.M. , Weibe Andrew
Published: October 2024

Data is both a worldmaking and a dismantling force. This panel brings together four approaches to queer data studies. The first talk focuses on genealogies of queer data studies, providing its intellectual history and foundations. The second talk examines a historically significant moment of queer data activism during the height of the HIV/ AIDS pandemic. The third talk examines current queer metadata interventions for libraries and archives. Finally, guided by queer approaches to computational data, the fourth talk focuses on forms of identification for Indigenous queer subjects in archives.
What Does It Mean to “Misuse” Research Data?
Authors: Pasquetto Irene V , Thomer Andrea , Acker Amelia , Chtena Natascha , Desai Meera
Published: October 2024

In this panel, we will discuss how “data misuse” is understood across different disciplines, and in particular digital curation, critical data studies, scholarly communication, and algorithmic fairness. The audience will be invited to contribute to the discussion by reporting on their own experience with data misuse, and brainstorming potential interventions to prevent misuse. Controversial reuses of open research data are emerging, including exploitation of marginalized communities, geo privacy violations, and perpetuation of harmful stereotypes. Incidents of data misuse hinder scientific progress and erode public trust, yet defining misuse remains challenging as one community's misuse might be another's best practice. The development of a shared framework to understand when, how, and why misuse of research data occurs can help science stakeholders decide when and how to release crucial research data, evaluate the potential for misuse, and tailor documentation of research data to prevent misuse. Our goal for this panel discussion is to take us a step closer to the development of such a theoretical framework for defining data misuse.
College Students' Metaphors for ChatGPT: An Exploratory Study
Authors: An Jiaxin , Bak Hyerin , Choi Wonchan , Zhang Yan , Stvilia Besiki
Published: October 2024

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.
Recognizing Implicitly Toxic Content Based on Multiple Attention Mechanisms
Authors: Deng Shengli , Wang Fan , Wang Haowei , Cai Yunna
Published: October 2024

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.
Can I Collaborate with You? An Investigation of Trust in Generative AI
Authors: Ding Zih‐Yan , Chiu Ming‐Hsin
Published: October 2024

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.
The Obsolescence and Time Lag between Early Publications and Non‐Early Publications
Authors: Dong Huei‐Ru , Huang Mu‐Hsuan , Lo Szu‐Chia
Published: October 2024

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.
Educational Use of AI Chatbot Responses: Exploring Situational Relevance in Higher Education
Authors: Hyun Hong Bo , Manman Luo
Published: October 2024

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.
Tools for Integrating Data by Complex, Dynamic Categories
Authors: Hruschka Daniel , Cheng Yi‐Yun , Hsiao I‐Han , Bischoff Robert , Peeples Matthew , Kasi Harsha , Huang Cindy
Published: October 2024

A key challenge in conducting comparative analyses across social units, such as religions, ethnicities, or cultures, 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.
People First Services: A Qualitative Study on Library Consolidations
Authors: Johnson Melissa E. , Lanford Michael
Published: October 2024

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 universities. 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.
Interaction with Information in the Japanese Job‐Seeking Practice: A Study of Female College Students
Authors: Hitoshi Kamada
Published: October 2024

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.
Enhancing Accessibility in Archives of Developing Countries: Strategies for Inclusive Information Access in Pakistani Context
Authors: Khan Muhammad Tehmash , Ullah Midrar
Published: October 2024

This study explores these challenges, focusing on language diversity, literacy levels, and technological infrastructure. Sindh's linguistic diversity, with Sindhi, Urdu, and English as primary languages, creates barriers for users not proficient in multiple languages. The need for translation services, limited by financial constraints, further complicates access. Additionally, literacy disparities between urban and rural areas hinder engagement with textual materials, necessitating educational outreach programs that require substantial investment. Technological barriers, including limited internet access and digital devices in rural areas, exacerbate these challenges. This research proposes strategies to enhance accessibility in Sindh Archives, such as multilingual cataloging, audiovisual resources, community outreach programs, mobile libraries, user‐friendly digital interfaces, collaborative partnerships, and capacity‐building initiatives. These strategies aim to promote equitable access to archival resources, enabling broader participation in research and education. By implementing these approaches, the rich cultural heritage of Sindh can be preserved and made accessible for future generations. The Sindh Archives in Karachi, with its extensive collection of historical documents, serves as a case study to illustrate the potential impact of these strategies.
The Impact of DEI Work on Retention of Librarians: A Moderated Mediation SEM Model
Authors: Kim Sunha , VanScoy Amy , Ding Weiyi , Crabtree Ayiana
Published: October 2024

This study explores the effects of diversity, equity, and inclusion (DEI) 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 Structural Equation Modeling (SEM) 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.
How Can Academic Librarians Support Generative AI Literacy: An Analysis of Library Guides Using the ACRL Information Literacy Framework
Authors: Ko Chun Ru , Chiu Ming‐Hsin (Phoebe)
Published: October 2024

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.
AI Transparency in Academic Search Systems: An Initial Exploration
Authors: Liu Yifan , Sullivan Peter , Sinnamon Luanne
Published: October 2024

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.
Securing Repair: Examining Cybersecurity's Influence on the Right to Repair
Authors: Mann Jasvinder , Centivany Alissa
Published: October 2024

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.
Examining the Impact of Brenda Dervin's Sense‐Making Methodology across Different Fields
Authors: Mooney Hannah , Agarwal Naresh Kumar
Published: October 2024

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.
The AI Divide in Norrland, Sweden
Authors: Sheldrick Susan , McKay Dana , Chang Shanton , Kurnia Sherah
Published: October 2024

Small and Medium Enterprises (SMEs) 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 Artificial Intelligence (AI) into 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.
Developed Countries Dominate Leading Roles in International Scientific Collaborations: Evidence from Scholars' Self‐Reported Contribution in Publications
Authors: Tian Yifan , Bu Yi
Published: October 2024

This study investigates the inequality of roles in international scientific collaboration, revealing that scholars from developed countries dominate leadership positions. Using data from high‐impact journals, we find significant underrepresentation of scholars from developing countries who often occupy supporting roles. After controlling authors' seniority, productivity, and scientific impact, developed country scholars are still more likely to lead research teams, perhaps because of the North–South disparity in academic resource distribution.
Responsible Practices for Crowdsourced Transcription and Accessibility for Web‐based Library, Archive, and Museum Content
Authors: Van Hyning Victoria , Jordan J. Bern , Jones Mace
Published: October 2024

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).
Using Positionality to Address Student Bias in Information Seeking
Authors: Weeks Thomas C. , Johnson Melissa E.
Published: October 2024

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.
Enhancing Keyword Extraction from Academic Articles Using Highlights
Authors: Yi Xiang , Xinyi Yan , Zhang Chengzhi
Published: October 2024

Keywords facilitate rapid comprehension of academic papers for scholars, enhancing research efficiency. As some papers lack author‐assigned keywords, automated keyword extraction becomes crucial. Addressing the limited utilization of external paper information beyond titles and abstracts in prior studies, this research proposed leveraging the highlights section, which summarizes contributions and novelties, for unsupervised keyword extraction. We demonstrated performance improvements across three unsupervised keyword extraction models by integrating highlight information into abstracts. Further analysis indicates we can filter out irrelevant abstract sentences based on their semantic similarity to the highlights. Overall, this study pioneers the exploration of utilizing paper highlights for keyword extraction, simultaneously boosting unsupervised keyword extraction performance and providing insights for other abstract‐based text mining applications.
The Construction and Application of Evaluation Index System of Rural Public Digital Cultural Service Accessibility
Authors: Xu Lei , Wang Meng , Sun Honglei , Hua Yuwen
Published: October 2024

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.
Determining Trust in Information: Initial Literature Review
Authors: Zimmerman Tara , Allen Jeff
Published: October 2024

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.
The Effects of Material‐Filled Voids on Detonation Wave Shape in Rubberized RDX Explosives
Authors: Lawrence Joseph R. , Koeblitz Austin D. , Montoya Gabriel A. , Srinidhi Pradyun , Son Steven F.
Published: October 2024

The sensitivity of explosives is affected by inhomogeneities within the material. This is evident in the increased shock sensitivity of explosives with slightly lower densities resulting from an increased number of hotspots. The influence of hotspots on explosive initiation has been well studied; however, few studies have been conducted on the effect of intermediate‐sized voids (0.1–10 mm) on a propagating detonation wave. Cylindrical voids filled with air have been studied for diameters ranging from 0.3 mm to 0.8 mm for both 1,3,5,7‐tetranitro‐1,3,5,7‐tetrazocane (HMX) and 1,3,5‐trinitro‐1,3,5‐triazinane (RDX)‐based rubberized explosives. Continuing the investigation into single cylindrical voids, this study examined the effects of 0.5 mm diameter voids filled with different inert cylindrical metals on the detonation wave shape for an RDX‐based rubberized explosive. The metals selected for experiments were 1066 aluminum, brass, copper, and tungsten. The propagation of the detonation wave was captured using a digital streak camera. Experimental results showed that the extent of detonation wave shaping was closely tied to the density differential between the bulk explosive and metal insert. Forty‐four different filler materials, including non‐metals, were simulated using a hydrodynamic code to further analyze material inclusion effects. The main factors hypothesized to be of interest were bulk sound speed, shock impedance, and filler material density. We found that the local detonation delay could be correlated fairly well to a ratio of bulk sound speed and density. Understanding the influence of material inclusions on detonation performance and wave shape allows for tailoring of detonations.
Does size matter? A single institution's comparison of Aquablation in prostates greater than or less than 150 mL
Authors: Ringler Renee , Gangwish David , Horning Paul , Kuperus Joshua , Palmateer Greg , Zwaans Bernadette M. M. , Hafron Jason , Peters Kenneth M.
Published: October 2024

Background

Surgical management options for lower urinary tract symptoms due to benign prostatic hypertension have remained limited in prostates of large volume. The advent of the Aquablation has created a potential minimally invasive option for treatment in prostates of all volumes. Thus, this study aims to evaluate outcomes and complications of Aquablation in clinical practice based on prostate volume.

Methods

Collected variables included adverse events with Clavien‐Dindo classifications, transfusion rates, surgical retreatment rates, continued medication use postoperatively, and International Prostate Symptom Score (IPSS) with Quality‐of‐Life indicator. Aquablations were stratified by preoperative prostate volume.

Results

One hundred seventy‐four men were included in the study. The average postop decrease in IPSS was 10.28, with a 2.02 point decrease in Quality‐of‐Life at 1 year. Postop increase in peak urinary flow rate was 7.65 mL/s for an average of 16.44 mL/s. Hemoglobin drop average was 1.78 g/dL, but only 2.3% of patients required a transfusion. 12.9% of patients required surgical retreatment. Six months after Aquablation, 22.9% and 12.9% of patients continued taking Alpha‐blockers and Androgen receptor inhibitors, respectively. Adverse events occurred in 33 patients (19.0%). Eighteen patients were excluded from secondary analysis due to unrecorded prostate volume, leaving 123 with volumes <150 mL and 33 with volumes ≥150 mL. Groups were comparable with respect to length of stay, adverse events, blood transfusion, IPSS with Quality‐of‐Life indicator preoperative and postoperative, postop peak urinary flow, and postop alpha‐blocker use. Larger prostates had a higher rate of adverse events, retreatment, postop hemoglobin drop, and postop use of finasteride.

Conclusions

Aquablation is a viable treatment option for benign prostatic hyperplasia. Efficacy, safety, subjective outcomes, and adverse event rates were not impacted by prostate volume. However, size does matter as prostates ≥150 mL had higher surgical retreatment rates and postop finasteride use.
Impact of broader ecological and socio‐environmental components on Aedes mosquito population dynamics: a spatial–temporal longitudinal study
Authors: Wang Yifan , Wang Xin , Wang Zhuonan , Chen Zhou , Liu Nannan
Published: October 2024

BACKGROUND

The increasing spread of mosquito‐borne diseases is a significant problem globally, but mosquito management strategies are less efficient. Therefore, a comprehensive understanding of the population dynamics of Aedes mosquito is essential for improving mosquito management strategies. Constructing a model to understand Aedes mosquito development in response to environmental factors is crucial to addressing these challenges.

RESULTS

An extensive data set on Aedes spp. mosquito populations was constructed, considering the environmental factors temperature, water vapor pressure, wind speed, daylength, and rainfall. This data set, compiled from mosquito collections over a period of four years across multiple locations in Alabama, USA, facilitated the prediction of mosquito dynamics. The random forest model was used to explain mosquito population changes in response to these factors. These findings indicated that temperature, daylength, and water vapor pressure had the most significant impacts on mosquito population dynamics. The model also allowed predictions of mosquito population changes over time and across different geographic regions, extending beyond Alabama to the southeastern USA.

CONCLUSION

This study provided valuable insights into the impacts of environmental factors on mosquito populations. This novel approach using machine learning and the random forest model will enable researchers to predict future mosquito populations and contribute to developing more‐effective strategies for mosquito management. © 2024 Society of Chemical Industry.
Is the GFR‐based scaling approach adequate for predicting pediatric renal clearance of drugs with passive tubular reabsorption? Insights from PBPK modeling
Authors: Li Sanwang , Ye Xuexin , Wang Qiushi , Cheng Zeneng , Liu Feiyan , Xie Feifan
Published: October 2024

Empirical maturation models (e.g., Johnson and Rhodin models) for glomerular filtration rate (GFR) are commonly used as scaling factors for predicting pediatric renal clearance, but their predictive performance for drugs featured with tubular reabsorption is poorly understood. This study investigated the adequacy of GFR‐based scaling models for predicting pediatric renal clearance in drugs with passive tubular reabsorption by comparing with a mechanistic kidney model (Mech‐KiM) that encompasses the physiological processes of glomerular filtration, tubular secretion, and reabsorption. The analysis utilized hypothetical drugs with varying fractions of tubular reabsorption (Freabs), alongside the model drug metronidazole, which has a Freabs of 96%. Our simulations showed that when Freabs is ≤70%, the discrepancies between the GFR‐based scaling methods and the Mech‐KiM model in predicting pediatric renal clearance were generally within a twofold range throughout childhood. However, for drugs with substantial tubular reabsorption (e.g., Freabs > 70%), discrepancies greater than twofold were observed between the GFR‐based scaling methods and the Mech‐KiM model in predicting renal clearance for young children. In neonates, the differences ranged from 5‐ to 10‐fold when the adult Freabs was 95%. Pediatric physiologically based pharmacokinetic (PBPK) modeling of metronidazole revealed that using a GFR‐based scaling method (Johnson model) significantly overestimated drug concentrations in children under 2 months, whereas utilizing the Mech‐KiM model for renal clearance predictions yielded estimates closely aligned with observed concentrations. Our study demonstrates that using GFR‐based scaling models to predict pediatric renal clearance might be inadequate for drugs with extensive passive tubular reabsorption (e.g., Freabs > 70%).