Conference Agenda (All times are shown in Mountain Daylight Time (MDT) unless otherwise noted)

Connecting the conference theme “Putting People First: Responsibility, Reciprocity, and Care in Information Science Research and Practice,” this research emphasizes the importance of centering our participants, their experiences, and their well-being. While Information Science (IS) researchers and practitioners aim to understand information seeking and needs across vulnerable communities, this poster suggests using “the vulnerable observer” (Behar, 1997) as a framework to encourage IS researchers to acknowledge and evoke the researcher’s own emotional involvement with their participants. Through the study of vehicle residents’ information practices as a case study, this work draws on various ethnographic methods, paired with information horizon interviews with 22 participants, utilizing trust, reciprocity, and positionality as tools to introduce the researcher’s own vulnerability when working with vulnerable populations. Preliminary findings highlight participants’ communities as valuable information sources some of the most emotionally vulnerable parts of participants’ conversations which impact their information seeking practices. A brief conclusion reiterates the importance of building deep rapport with vulnerable participants.

Parents’ concerns about children’s digital media use were investigated using data from semi-structured interviews with 17 parents of children ages five to 11 at three branches of a U.S. urban public library system. Data were analyzed using collaborative inductive thematic analysis and approached with the concept of culturally-constructed anxieties about new media as an analyzing lens. The most common concerns included worries about exposure to inappropriate content, worries about digital media taking up time that children would otherwise spend engaging in more meaningful activities, concerns about safety and privacy, and worries about negative effects on children’s behaviors, attitudes, and social skills. Further analysis showed parents' deeper concern for children’s healthy development to underlie these narrower concerns. The authors conclude with the recommendation to shift the framing of discourse around parenting with digital media from risk protection to digital media education. Such a shift could raise awareness that framing children and digital media only in terms of risks is overly simplistic, and it could help parents come to understand that children’s digital media use is not just risky but also an opportunity for children to derive educational and social benefits, and learn how to operate in a digital media-dominated information ecosystem.

In this paper, we use a critical informatics approach to investigate institutional disinformation around 2022-23 labor organizing at three higher education institutions: Rutgers University, Temple University, and the University of California. Our contribution to the study of disinformation is the application of critical informatics perspectives that attend to structural power dynamics of disinformation within an institutional context. Understanding the political economic dynamics of disinformation and how these dynamics function can help more solidly contextualize and clarify how and why disinformation exists across different information systems, so that solutions to this social and institutional problem of disinformation can be more appropriately addressed and understood. The study describes disinformation tactics employed by institutional leaders during higher education labor organizing including: non-performative commitments to “community”, legal threats, misleading victories, and elite capture.

A key challenge in conducting comparative analyses across social units, such as ethnicities, cultures, or religions, is that data on these units is often encoded in distinct and incompatible formats across diverse datasets. This can involve simple differences in the variables and values used to encode these units (e.g., Roman Catholic is V130 = 1 vs. Q98A = 2 in two different datasets) or differences in the resolutions at which units are encoded (Maya vs. Kaqchikel Maya). These disparate encodings can create substantial challenges for the efficiency and transparency of data syntheses across diverse datasets. We introduce a user-friendly set of tools to help users translate four kinds of categories (religion, ethnicity, language, and subdistrict) across multiple, external datasets. We outline the platform’s key functions and current progress, as well as long-range goals for the platform.