Lessons from research on the governance of misinformation in everyday life apply widely to individual and community experiences.
Lessons from research on the governance of misinformation in everyday life apply widely to individual and community experiences.
In a landscape saturated with data, the challenge lies in making information meaningful. How data is structured, visualized, and contextualized determines whether it merely informs or truly influences perception and decision-making. However, raw data alone does not communicate meaning. Traditional data visualizations, such as bar charts, scatter plots, and heat maps, often fail to engage audiences emotionally and cognitively. This is where data storytelling emerges as a transformative approach.
Thinking of communities as “information poor” misrepresents the reality of systemic exclusion. Instead, marginalized communities have been intentionally and unintentionally excluded from mainstream information infrastructures. This exclusion is not due to a lack of knowledge on the part of marginalized communities but rather a reflection of structural barriers that limit access to institutionalized information flows. We need to recognize the existence and prevalence of information precarity, and then we need to radically alter how we plan and carry out projects, research, and outreach with—not for—marginalized communities.
Many educational institutions use learning management systems (LMSs), which may track and analyze a student’s every click, assignment submission, and even location; this also makes them useful for learning analytics, the collection and analysis of student data in the name of supporting learning and teaching. While students may know that LMSs collect their data, they often don’t understand the extent of just how much data these systems collect! Yet, it’s not hard to imagine what the scope of LMS data collection means for student privacy. This imbalance highlights the urgent need for greater transparency and critical data education in the use of educational technologies.
Microfinance institutions (MFIs) possess a unique and powerful tool for combating the localized challenges of climate change: the social capital of their borrowers. While MFIs rightly prioritize poverty reduction, they can and should simultaneously leverage this inherent social capital to address the growing threat of climate change.
Our analysis raises critical questions: Why do segments of the general public express such hostility towards women in digital spaces? Are these sentiments a mere reflection of broader societal attitudes, or are they amplified by the affordances of online anonymity and lack of accountability? What structural interventions are necessary to dismantle these patterns of digital violence? Understanding these underlying factors is imperative to formulating sustainable strategies to combat cyberbullying.
While keeping pace with the new developments in generative AI and LLM reasoning models is in itself a challenge, new ‘deep research’ agents are beginning to surface at a remarkably rapid rate.
During the current moment, as generative AI dominates our thinking, both for its extraordinary performance and serious flaws, a new direction is needed. The way forward may involve looking backward. The addressing the deficiencies of generative AI would benefit from reviewing, and incorporating, some of the lessons from expert system development during the late 20th century.
COVID-19 has become the catalyst of health MIDI (misinformation and disinformation) on the World Wide Web. It is difficult for people to know what to believe. If people act on wrong information, their health may get worse. We need to be ready when the next health emergency comes. We need to know how to find good health information.
Christina J. Steffy and Meg Massey wrote in the October 2024 issue of C&RL News about a challenge that has plagued Information Science education and professional librarians for many years. They specifically focus on one job position at the American Library Association (ALA) and pose reasonable questions to all of us. Can the specialized training of library schools (even if they are innovative and have joined the iSchool movement) prepare graduates for the jobs available in the ALA? Is it not possible to find a person who is more qualified to hold the desired job among the graduates of the adjacent fields, such as Computer Science and Management? Basically, should librarians run the ALA?
During the current moment, as generative AI dominates our thinking, both for its extraordinary performance and serious flaws, a new direction is needed. The way forward may involve looking backward. The addressing the deficiencies of generative AI would benefit from reviewing, and incorporating, some of the lessons from expert system development during the late 20th century.
Social media has not helped us build a strong and resilient tribe with diverse skills and resources. It is now simply geared to help us find others that “like” the same things as we do, which is not the connection points for tribes.
Many educational institutions use learning management systems (LMSs), which may track and analyze a student’s every click, assignment submission, and even location; this also makes them useful for learning analytics, the collection and analysis of student data in the name of supporting learning and teaching. While students may know that LMSs collect their data, they often don’t understand the extent of just how much data these systems collect! Yet, it’s not hard to imagine what the scope of LMS data collection means for student privacy. This imbalance highlights the urgent need for greater transparency and critical data education in the use of educational technologies.
Peppa Pig books have seamlessly blended fun and learning, making them an essential part of early childhood literacy. But what exactly makes these books so beloved, and how do they foster essential reading skills? Let’s dive into the world of Peppa Pig and explore the magic behind these engaging books.
How might we understand the way in which research and education can be used as a positive force in bringing the world closer together? This difficult question is one that those of us within digital humanities at University College London want to address. Looking at our institutional history can help to clarify this. The history of digital humanities is gaining attention, with an increasing focus on understanding its development and recognising its role of addressing diversity, gender, race, and linguistic backgrounds in knowledge production, and promoting inclusive representation.
How can sexual and reproductive justice be achieved in communities where youth struggle to access Sexual and Reproductive Health and Rights (SRHR) services? When young people receive accurate, stigma-free SRHR information, they make informed choices, improve their well-being, and advocate for their rights.
Narayan’s influence extends beyond fiction; his essays and memoir offer profound insights into Indian culture, society, and his own life. His memoir, “My Days,” provides a candid glimpse into his life, offering readers a deeper understanding of the man behind the pen. Despite international acclaim, Narayan stayed true to his Indian identity, avoiding literary trends and focusing on universal truths. His commitment to authenticity earned widespread respect.
The library is a technological hub where cutting-edge devices are introduced to the world. Contrary to the unpopular rumour, especially prevalent in Africa, that libraries are outdated and librarians’ services are no longer needed, the library is a phoenix that evolves and adapts to the emerging modern world. Today, it is more relevant than ever, especially with the growing digital divide caused by socio-economic disparities, geographical disadvantages, infrastructure inequality, skillset deficiencies, age factors, and more.
En las últimas dos décadas, la IA ha avanzado a pasos agigantados hasta el punto de que estamos hablando seriamente de que es tan inteligente como los humanos o incluso que los supera. Pero antes de dejarnos llevar demasiado por esa emoción o ese miedo, es importante que miremos hacia dentro y nos preguntemos: ¿qué nos hace inteligentes, conscientes y humanos?
Robots asesinos. Inteligencia artificial distópica. Rebeliones de máquinas. ¿Por qué nos fascina tanto la idea de que nuestros aparatos se vuelvan contra nosotros? En este artículo, desentraño nuestra obsesión con el ascenso de las máquinas y demuestro que gran parte de su atractivo es en realidad la proyección de nuestros propios miedos y dilemas.
las competencias para la investigación en línea de los estudiantes en Chile, no necesariamente están suficientemente desarrolladas para abordar sus tareas escolares.
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.,
自先秦诸子开宗立派、传授学说以来，历代学者承袭旧学，开辟新篇，所著经史辞章，累若山海。因此，目录之学成为“学中第一紧要事”，古代读书人从目录中获得“即类求书，因书究学”的治学要领；今天要进入古籍的世界，我们更需要问途于目录。
