Computational Social Welfare: Application of Big Data in Research and Practice

Columbia University - 2018-2019 Policy and Society in Contemporary China Lecture Series

Lecture

March 1, 2019
12:15pm to 1:45pm
New York, NY

 

It’s no longer unusual to hear about the application of big data in business, finance, and government.  However, relatively few attention is given to using big data to provide benefits in social work and welfare.  Employed to its fullest, big data has the potential to improve the quality of life of vulnerable populations by improving access to services, improving organizational efficiency, improving the quality of public and private programs, and reducing costs of services. Using Guizhou Berkeley Big Data Innovation Research Center (GBIC) as a case study example, this presentation provides an overview of findings drawn from an innovative model of big data science, applying data management and analytics to inform and improve the way society can support the vulnerable population.

Free and open to the public. CEUs available. Livestream Available. Registration required.

This event is part of the 2018-2019 Policy and Society in Contemporary China Lecture Series, cosponsored by the China Center for Social Policy and Weatherhead East Asian Institute, and supported by CSSW, GSAPP, and Columbia Global Centers | Beijing.

Speaker(s)

Marla Stuart

Alumni - BIDS Data Science Fellow

At UC Berkeley, Marla Stuart was a BIDS Data Science Fellow working with the Guizhou Berkeley Big Data Innovation Research Center (GBIC), a research hub based in Guizhou Province, China, dedicated to improving the health and well-being of China’s population. Her work with the GBIC focused on developing actionable programmatic and policy recommendations for consideration by government agencies. She led the GBIC computational lab, which collected, wrangled and modeled data from government bureaus and other sources to support the research goals of agency partners and GBIC faculty. Her own research concentrated on understanding the applicability of data science approaches in social welfare research and practice settings.
 
Previously, Marla had spent twenty years conducting practice-based research in public and private organizations that provide health and human services in vulnerable communities. This included fifteen years with the Navajo Nation in Arizona, where she worked with local communities to develop health and social services evaluation approaches derived from traditional Navajo philosophy and values.
 
Marla earned her Masters of Social Work from the University of Washington in Seattle with a focus on planned social change. She received her PhD from the School of Social Welfare at Berkeley. Her dissertation explored government efforts to scale the use of evidence-based services. It used public government records and crowd-sourced and computational data-extraction methods to create measures of these strategies. It assessed the relative effects of these public strategies on scaling progress using time-to-event analysis. It found that county governments are well positioned to implement scaling strategies and that the proportion of social service providers adopting evidence-informed services can be increased as can the proportion of county funding directed to these organizations. This study design is highly replicable and as such provides a general model to apply to other local environments to identify common county levers that effectively promote the scaling of evidence-informed social services.

Julian Chun-Chung Chow

Hutto-Patterson Charitable Foundation Professor at the School of Social Welfare, UC Berkeley

Julian Chun-Chung Chow is Hutto-Patterson Charitable Foundation Professor at the School of Social Welfare at the University of California, Berkeley. Dr. Chow received a B.A. in Sociology/Social Work from Tunghai University in Taiwan and a Master's and doctorate degrees in Social Welfare from Case Western Reserve University. Specializing in community analysis, his research interests include the study of social and mental health services delivery in ethnic and immigrant neighborhoods, particularly in Asian American communities. His current research examines social services for China’s migrant populations as well as social work education and its development in China. He has published over 80 manuscripts and delivered over 200 national, international, and invited presentations. He has been active serving on many national, state, and local organizations, addressing issues such as welfare reform, poverty, immigration, health, and mental health. He is a former Fulbright Scholar and a Fellow of the Society for Social Work and Research. He was awarded the 2016 Outstanding American by Choice Recognition from the U.S. Citizenship and Immigration Services. Most recently, he is director of Guizhou Berkeley Big Data Innovation Research Center (GBIC), a research hub based in Guizhou Province, China that is dedicated to improving the health and well-being of China’s population. GBIC focuses on developing actionable programmatic and policy recommendations for consideration by government agencies.