Community Informatics

Mapping Risk Work and Designing Technologies to Support it in CSCW Research

This workshop brings together CSCW scholars of various domains, such as medicine and healthcare, disaster planning, and public safety, to consider different dimensions of risk work and their implications on computing. Risk work encompasses the practices through which workers assess, manage, and mitigate potential harms in situations framed by uncertainty. In the face of a pervasive rhetoric of crisis, risk work is expanding and evolving as workers and laypeople are increasingly charged with preventing, predicting, and communicating risks.

From Open‑Ended Text to Taxonomy: An LLM‑based Framework for Information Sources for Disability Services

People with disabilities (PWD) and their family members often find it difficult to find information about available services. One of the approaches to address this information access problem is by understanding the ecology of available information sources. However, identifying the landscape of information sources is challenging due to the variety of sources and their varying visibility. This study proposes a computational approach to processing open-ended survey answers by constructing a hierarchical taxonomy of information sources.

News Deserts as Information Problems: A Case Study of Local News Coverage in Alabama

This paper explores the phenomenon of news deserts as information problems to navigate research opportunities and theorize its dynamics. Drawing on the theory of local information landscapes, news deserts are conceptualized as more than merely an absence of news organizations or content; rather, emphasizing the structural and material dimensions of local news ecosystems, such as fragmentation, transience, and inconsistent distribution. We argue that news deserts should be understood as material pre-conditions of people’s access, interpretation, and engagement with information.

SAFETI: Strategic Analysis for Fine-granular Injury and Fatality PrEvenTion Insight

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.

Exploring Librarians' Experiences and Perceptions of Public Library Data Work

This study investigates the experiences and concerns of public librarians responsible for statistical data management and explores strategies to address related challenges. In-depth interviews were conducted with ten librarians handling statistical tasks at public libraries in Metropolitan City A and Province B in the Yeongnam region. Findings revealed that librarians had established a systematic workflow involving data collection, internal verification, data entry, and external verification.

2024 Assessment of Virginia’s Information Ecology of the Disability Services System

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.

Understanding Information Managers: A Thematic Analysis of Information Challenges of Disability Service Providers

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.

Data Discretion: Screen-Level Bureaucrats and Municipal Decision-Making

Public servants tasked with implementing rules or policies on the street-level often make discretionary decisions based on local context. Lipsky has labeled them street-level bureaucrats. During the COVID-19 pandemic, as most face-to-face interactions facilitated by local government moved online, many street-level decisions were moved to screens, representing the actions of who Bouvins and Zouridis refer to as screen-level bureaucrats. Discretionary decision-making among public servants continued, but much of it centered on the collection, analysis, and use of data.

Exploring Domestic Workers’ Risk Work During the COVID-19 Pandemic

While many occupations turned to remote work during the COVID-19 pandemic, domestic work by definition requires workers to enter other people’s households, and they often work in close proximity to their employers. With domestic workers proactively handling COVID19 risks as part of their already precarious jobs, there is a need for a conceptual understanding of risk management to aid this occupational group during a public health crisis.

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