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The Inclusion of Disabled and Neurodiverse Ph.D. Students: Embracing Care and Kindness

Theory- and experience-grounded contemplations about the impact of the COVID-19 global health crisis were reflected in the recent study of disabled and neurodiverse Ph.D. students in information science programs in Canada and the U.S. that we conducted. Ph.D. students present a unique demographic in academia, blending the characteristics of students, researchers, and often teachers as well. Responses from 42 participants revealed a wide range of experiences in their doctoral programs that were both pandemic-specific and transcending the pandemic period.

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How Information Objects Can Create Information Inequity

Information plays an important role in helping people make informed decisions and advocate for themselves socially, economically, and politically. However, not everyone has fair or equitable access to information. Information science scholars term this information inequity—the individual and societal factors that limit an individual, group, or nation’s access to information. Addressing information inequities by identifying and rectifying their causes is essential to helping all people fully participate in our society and democratic system.

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Overcoming Language Barriers with Innovative Design for Multilingual Digital Platforms

Managing personal information in multiple languages is not just a convenience, it is a necessity. Personal Information Management (PIM) involves the organization, storage, retrieval, and use of personal data to support daily tasks and long-term goals. Yet, for many multilingual users, digital platforms fail to meet their needs, creating barriers to effective personal information management. Our study sheds light on these challenges and offers a vision for more inclusive, user-centered platforms.

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Dementia-Friendly Library Services & Sustainable Communities

We are in a global epidemic of persons living with dementia (PLWD), but libraries are already serving this growing and vulnerable population. More than 55 million people around the world live with Alzheimer’s Disease and related dementias. The estimated total global societal cost of dementia exceeds $1.3 trillion per year. 88% of PLWD report experiencing stigma and discrimination and dementia is more prevalent in minority communities, so dementia-friendly library services also support social justice.

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From Attraction to Subscription: Decoding How Knowledge Influencers Monetize Expertise

In an era where digital platforms dominate the exchange of ideas and services, knowledge influencers have emerged as a distinct phenomenon, blending expertise with online engagement to create and monetize knowledge-intensive content. Unlike conventional influencers who primarily endorse consumer goods, knowledge influencers focus on delivering value through educational and professional insights, shaping user behaviors and driving subscriptions to self-created knowledge products.

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Librarians Do Not Take a Break from Research

Librarians are the polymaths of this century. They are voracious readers, consuming information from all fields. As information gatekeepers, their engagement in knowledge classification, cataloging, and organization grants them a birthright to know more than others. Librarians help create all professions, but no other profession creates librarians. This is a unique quality that makes librarians stand out and excel among other fields. The secret lies in the fact that librarians never take a break from research.

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EditorialFeatured

Can We Really Control AI?

AI is playing an increasingly larger part in our lives and the world around us. By some projections, we will have AGI, or Artificial General Intelligence, in less than a decade. Some are even arguing we are already there. Regardless of this timeline, it is clear that AI unchecked has potentials to cause great harms. Can we control or contain AI such that we can stop those harms? It’s not easy.

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Towards human-like perception: Learning structural causal model in heterogeneous graph

In recent years, the growing demand for modeling complex systems has brought heterogeneous graph neural networks (HGNNs) into the spotlight. However, existing methods often suffer from fixed inference processes and spurious correlations, limiting their interpretability and generalization ability. To address these challenges, this study proposes a novel heterogeneous graph learning framework that simulates human perception and decision-making processes, enhancing both predictive performance and interpretability.

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