Webinar: An argument for informational comparability of individuals and an attempt to capture and explain their differences
May 13, 2022, 9:00-11:00 AM (China UTC+8)
Sponsored by ASIS&T AP Chapter
Presenter: Yu Liangzhi
Information inequality, or the disparity between society’s information rich and poor has been regarded as one of the central issues in information society. This challenges library and information science to address two important questions: Is it possible to compare and define people in relatively stable informational terms? Can library and information science offer concepts and theories by which individuals’ existence in the information sphere of the world is described and understood? This presentation argues for positive answers to both questions, counter to implications of the subjective view of information which sees information as situational; it also explicates a concept (called an individual’s information world) and a framework (featuring practice-experience-mind interactions) as examples to demonstrate the possibility. It calls on library and information science to balance its situational approach to information, information behavior and information technology with due considerations of larger pictures, not least people’s relatively stable information-related characteristics.
Dr. Yu Liangzhi is professor of library and information science at the department of information resource management, Nankai University. She earned her doctoral degree from Loughborough University, UK, her Master and Bachelor degrees from East China Normal University. Her research interest includes conceptual issues of library and information science, and information access disparity in society. Her recent research focuses on the effect of pedagogical models on the cultivation of information-rich/poor citizens for information society. Her research has been published in JASIS&T, JDoc, Journal of Librarianship and Information Science, Libri, and so on. She also won Emerald Outstanding Paper Award 2016.
Please scan this QR code to register before May 11, 2022, 5:00 PM (China UTC+8).
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