2024 - Information Matters

December 16, 2024 Azeez Adeoye

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.

Featured INFideos

December 16, 2024 Jenna Hartel

A Round-Up of Holiday Gifts from INFIDEOS

In mid-December of each year, my YouTube channel, INFIDEOS, gives a gift to its subscribers and viewers. These multimedia creations are freely available to everyone in Information Science and are especially suited for students of the field.

Editorial Featured

December 15, 2024 Chirag Shah

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.

INFideos

December 15, 2024 Jenna Hartel

Surfacing the ‘Silent Foundation’

The 12th episode in the What Makes This Paper Great? video series features “Surfacing the ‘Silent Foundation’: Which Information Behaviour Theories are Relevant to Public Library Reference Service?” by Amy VanScoy, Africa S. Hands, Katarina Švab, and Tanja Merčun. The paper was presented at the 2024 Information Seeking in Context conference in Aalborg, Denmark. A new 12-minute video at INFIDEOS takes viewers through the highlights of the paper. For fun, an opinionated but cuddly group of Library and Information Science (LIS) students join the virtual conversation.

Chinese

December 13, 2024 Zhuoren Jiang

像人类一样感知：异构图中的结构因果模型学习

近年来，随着复杂系统建模需求的增长，异构图神经网络逐渐成为研究热点。然而，现有方法通常存在固定推理流程和虚假相关性等问题，限制了模型的可解释性和泛化能力。为此，我们提出了一种新颖的异构图学习框架，通过模拟人类感知与决策过程，增强对任务的预测性能与结果解释能力。核心研究思路我们提出的方法基于以下关键步骤： 1.语义变量构建：通过图模式与元路径提取出易于人类理解的语义变量，例如论文、作者和会议等。2.因果关系挖掘：采用结构方程模型，自动发现变量之间的因果关系，并利用学习到的因果关系进行预测。3.目标任务预测：通过逆推算法，将语义变量的因果关系转化为目标任务的预测结果。数据集与实验验证研究在三个公开数据集（DBLP、ACM和IMDB）上进行了验证，这些数据集涵盖学术、社交和电影推荐等多种场景： DBLP：预测作者的研究领域。ACM：预测论文的学术类别。IMDB：预测电影的类型。通过引入三种偏差（同质性、度分布、特征分布），验证了模型在不同数据分布下的泛化能力。实验表明，提出的模型在所有设置下表现稳定，泛化能力显著优于现有方法。主要研究成果 1.泛化性：模型在不同偏差条件下保持较高的预测性能，表现出卓越的适应能力。 2.可解释性：通过因果关系图清晰展示变量间的影响逻辑。例如，作者研究领域与发表论文的会议密切相关，模型捕捉到这一直观规律，并验证了因果推理结果与专家经验的一致性。实际应用前景该框架在技术挖掘、金融分析、政策评估等领域具有广泛应用潜力。例如，通过因果关系图辅助技术创新决策，或在金融分析中解释影响因素以提升透明度。此外，其高度可解释性为改进模型逻辑、提升可信度提供了可能性。总结异构图学习正在为复杂系统建模与分析带来新的可能性。本研究通过结合因果推理技术与异构图神经网络，不仅实现了对复杂任务的准确预测，还增强了对模型推理过程的理解，为人工智能在实际问题中的应用提供了新的视角。本文基于以下成果写作完成： Lin, T., Song, K.,

Featured Translation

December 11, 2024 Zhuoren Jiang

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.

Featured Original

December 10, 2024 Dariush Alimohammadi

Religious Cults as Builders of Marginalized Communities

Religious cults represent the worst form of marginalization. Libraries must help people avoid falling into the trap of cults by informing them. Otherwise, it becomes almost impossible to return cult members to normal life, and even if a person leaves a cult after several decades, the effects of living in a closed system will keep them marginalized forever.

Translation

December 5, 2024 Md Anwarul Islam

Exploring Women’s Health Information Literacy with AI: A South Asian Study

The relationship between AI and people’s health information is increasingly significant, and AI chatbot provides significantly more accurate answers to patients. However, while technology can help, it is up to people to decide how they want to use it. Even an AI tool like ChatGPT says “ChatGPT can make mistakes. Consider checking important information.” Using AI tools to make health-related decisions requires a good understanding of the information these tools provide. The project “AI and Health Information Literacy: A study exploring the perceived usefulness, and readiness among women in South Asia” aims to address the questions like “How do women in South Asia (SA) perceive the usefulness of AI in enhancing health information literacy?” and “What factors influence their readiness to adopt AI-driven health information technologies?”

Translation

December 4, 2024 Jette Seiden Hyldegård

Information Avoidance: Out of the Shadow of Information Seeking

Up till now our understanding of information avoidance has remained fragmentary. Researchers have been unable to give a single coherent definition of what IA actually is. With our critical conceptual review of IA, we sought to address this oversight by theorizing IA as an instance of human information practice—distinct from, but co-existing dynamically with information seeking.

Original

November 26, 2024 Sheng-Hsiang Lance Peng

Who Belongs in the Library? Reconsidering Academic Skills Tutors and Institutional Expectations

In this reflective piece, I share my experience as a post-PhD early-career researcher navigating the challenges of the academic job market, particularly in applying for academic skills tutor roles within university library teams. Despite an academic background and a range of experiences, I encountered repeated rejections, which led me to ask: Who belongs in the library?