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FeaturedTranslation
Heather O'Brien

Setting the Course and Embracing the Journey: Reflections on Knowledge Mobilization in the Canadian Research Context

Knowledge mobilization (KMb) is the movement of research findings between and within academic and non-academic settings. In a recent SSHRC-funded partner development project, Supporting Transparent and Open Research Engagement and Exchange (STOREE), we constructed a KMb plan as part of our funding application. Our work focused on making research more accessible, relevant to, and useful for non-academic audiences, and supporting scholars to change practices around research sharing. Reflecting on the project, team composition, how we worked together, sub-project processes and outcomes, and individual learnings, we gained insights on making alternative outputs and KMb more broadly.

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FeaturedTranslation
Xiaoqian Zhang

Health Information Without Borders: The Struggles and Strategies of Older Chinese Adults in Canada

Have you ever struggled to find the right health information, unsure of where to turn or what advice to trust? For many older Chinese adults in Canada, this challenge is even greater. They often face situations such as navigating a complex healthcare system, overcoming language barriers, and balancing traditional health beliefs with Western medical practices. These challenges can impact how they make health decisions and their overall well-being. Through in-depth interviews with 20 older Chinese adults in Canada, our research explores various factors related to how they seek and use health information. What did we uncover? Join us as we delve into their stories and the broader implications for health equity in Canada.

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FeaturedTranslation
Jaihyun Park

Rethinking Reuse in Data Lifecycle in the Age of Large Language Models

In the world we are living in, a digital world, some data slips past our awareness, but very little data ever truly disappears. As we, information scientists, are concerned with reproducibility and responsibility of research, data lifecycle models have been developed to manage the complexity. To foster open, transparent, and collaborative science, data is often archived in a repository at the end of the project according to such data lifecycle models. This is often followed by the last step of the lifecycle models, data reuse. Traditionally, this model is cyclical, with reused data leading to new questions and fueling subsequent rounds of research.

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FeaturedInfoFire
Shalini Urs

LLMs, AI, and the Future of Research Evaluation: A Conversation with Mike Thelwall on Informetrics and Research Impact

In this episode of InfoFire, I sit down with Professor Mike Thelwall, a well accomplished scholar of Informetrics, to explore the intersections of Large Language Models (LLMs) and research evaluation. We delve into how LLMs are reshaping the landscape of research assessment, examining the promises they hold and the challenges they present in ensuring fair, meaningful, and context-aware evaluations.

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FeaturedTranslation
Fatima Espinoza Vasquez

Transforming Ourselves, Transforming Inequity: Reimagining Partnerships for Information Justice

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.

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EducationFeaturedTranslation
Meghan Dowell

The Cost of Clicks: Cultivating Data-Awareness and Ethical LMS Practices in Higher Education 

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. 

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EditorialFeatured
Chirag Shah

Here Come Agents

An agent is an autonomous entity or program that takes preferences, instructions, or other forms of inputs from a user to accomplish specific tasks on their behalf. And there is a huge hype around agents these days, thanks to advancements in various GenAI technologies. As big and small companies and individual developers continue investing heavily in development and deployment of agents, we are often missing some of the basic considerations, including what problems are we solving and how users, their tasks, and their contexts are incorporated in these developments.

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