Purpose: This study investigated the relationship among information awareness, access, and socioeconomic deprivation in the context of local health information. Drawing on theories of information poverty and the digital divide, this study examined how socio-economic deprivation influences individuals’ ability to become aware of and access local health information.
SAFETI is the first Mason–DOLI Innovation Lab initiative that turns more than 15 years of detailed Virginia workplace-accident records into forward-looking, preventive insights. Using predictive models, the computational approach developed for SAFETI estimates the likelihood of a fatality occurring within a specific time frame and sector, along with its associated probability. This shift from reactive to preventive measures is enabled by advanced spatio-temporal and predictive analytics.
As libraries and archives across the world are planning an increasingly digital records future, there is a critical need to strengthen digital and computational literacy and training for future librarians and archivists. Acute “skills and management gaps in libraries” have been recognised which highlight the need for greater automation in library work, the facilitation of computational research, and the need for library managers to understand and value the benefits of in-house data science skills.
Access to disability services information depends on many factors, from an individual’s digital literacy, social connections and physical mobility to the interface design of websites. However, it is also true that the availability of disability services information (e.g., how to apply for a Medicaid Waiver) and how such information is managed and provided to end users in Virginia are also critical factors that shape people’s information access. This assessment focuses on understanding the latter, namely, the “information ecology” of disability services in Virginia.
This paper details an investigation into the information management challenges encountered by disability service providers. Prior research has mainly focused on understanding the information management challenges related to user records or internal organizational systems. However, this study posits that the information access patterns of disability services users are significantly influenced by their interactions with service providers’ information management practices in localized settings.
This study leverages network modeling methodologies to examine the distribution of political inclinations in Korean middle school language textbooks from 1945 to 1953. The textbooks included in this study are 82 textbooks, where 37 of which from the U.S. military government period (1945-1948), 30 of which from the South Korean government establishment period (1948-1950), and 15 of which from the wartime period (1950-1953).
In November 2024, OpenAI released GPT- 4V(ision), which includes Optical Character Recognition (OCR) capabilities. Given that much of the data curation, processing, and cleaning can be managed through user-friendly prompts (i.e., chat), we aim to conduct an initial assessment of GPT-4V’s effectiveness in transcribing hand-written documents from the urban renewal collection. If GPT-4V can accurately digitize hand-written documents through carefully crafted prompts, it could become a valuable tool for nonexperts in transcribing historical documents on a large scale.
Information behavior research to date has mainly focused specific cases or representative surveys at the individual level, because each individual has unique contexts that shape their behavior. However, they have not fully benefited from aggregate-level analyses due to mainstream theories’ focus on a contextualized understanding of information. To address this gap, we adopt the theory of local information landscapes, that focuses on the material aspects of community dynamics, and analyze national-level aggregate data on book circulations in public libraries across South Korea.
Author keywords attached to academic papers are often used in intellectual structure analysis. However, the length and selection criteria for keywords vary across publications and, even some publishers do not require keywords for their articles. To explore the opportunity to overcome such keyword inconsistency issues, this study compared author keywords from papers focused on the digital divide with those extracted using the language models, BERT and ChatGPT.
The goal of the project is to understand information inequality across geographical regions in South Korea and visualize them using an AI-backed visualization tool. Our plan, spanning three years, revolves around the development of an intuitive platform for the purpose of visualizing these disparities. During the first year, we aim to construct comprehensive metrics for assessing the level of informational inequality, based on the theory of local information landscapes (LIL theory).