Data Science

Leveraging Large Language Models for Analyzing Judicial Disparities in China

October 8, 2024
Nanqin Ying
by Nanqin Ying. This study analyzes over 50 million judicial decisions from China’s Supreme People’s Court to examine disparities in legal representation and their impact on sentencing across provinces. Focusing on 290 000 drug-related cases, it employs large language models to differentiate between private attorneys and public defenders and assess their sentencing outcomes. The methodology combines advanced text processing with statistical analysis, using clustering to categorize cases by province and representation, and regression models to isolate the effect of legal representation from factors like drug quantity and regional policies. Findings reveal significant regional disparities in legal access driven by economic conditions, highlighting the need for reforms in China’s legal aid system to ensure equitable representation for marginalized groups and promote transparent judicial data for systemic improvements.

Understanding Adolescent Ethnic-Racial Identity: A Latent Profile Approach

September 24, 2024
Elaine (Hua) Luo
by Elaine Luo. As youth navigate an increasingly ethnoracially diverse society like the United States, their ethnic-racial identity (ERI) plays a crucial role in shaping various aspects of their development, including academic and psychosocial outcomes. In this post, I share insights from our recent study on adolescent ERI and youth adjustment. Using a person-centered approach, we identified four distinct ERI profiles: Strongly Diffused, Moderately Diffused, Balanced, and Achieved. Our findings revealed differences in educational motivation, school belonging, and expectations for discrimination across these profiles, highlighting the complexity of ERI development. Implications for caregivers, educators, and communities are also discussed.

Python Fundamentals: Parts 1-3

September 16, 2024, 2:00pm
This three-part interactive workshop series is your complete introduction to programming Python for people with little or no previous programming experience. By the end of the series, you will be able to apply your knowledge of basic principles of programming and data manipulation to a real-world social science application.

Anna Björklund

Senior Data Science Fellow 2024-2025, Data Science Fellow 2023-2024
Linguistics

I am a fifth-year PhD student in the Department of Linguistics with an areal interest in the Wintuan languages, traditionally spoken in the northern Sacramento Valley and now undergoing revitalization. My primary research interests are in leveraging archival recordings for the phonetic analysis of these under-documented languages, as well as designing tools to assist in their revitalization. I have worked as a linguistic consultant for the Paskenta Band of Nomlaki Indians since 2020 and the Wintu Tribe of Northern California since 2022. I received my MA in linguistics from UC...

Leah Lee

Senior Data Science Fellow 2024-2025, Data Science Fellow 2023-2024
Integrative Biology

I am a PhD candidate in the department of Integrative Biology. My research interest is at the intersection of biomechanics, entomology, and physiology. Currently I am studying how beetles use their shield-like forewings called elytra for flight, thermoregulation, and protection. Prior to UC Berkeley, I worked as a research assistant at Korea Institute of Ocean Science and Technology (KIOST), studying algae phylogenetics. I received my B.A. in Biology and Mathematics from Swarthmore College.

Alex Ramiller

Senior Data Science Fellow 2024-2025, Data Science Fellow 2023-2024
City and Regional Planning

I am a PhD Candidate in City and Regional Planning. My research focuses on the use of large administrative datasets to study residential mobility, neighborhood change, and housing access. I received a Master in Geography from the University of Washington and a Bachelor's in Economics and Geography from Macalester College. I have also consulted on analytical projects for several organizations including the San Francisco Federal Reserve Bank, PolicyLink, and the City of Seattle.

Farnam Mohebi

Data Science Fellow 2023-2024, Data Science for Social Justice Senior Fellow 2024
Haas School of Business

I am a PhD student at the Haas School of Business, University of California, Berkeley, and a researcher in the Department of Radiation Oncology at the University of California, San Francisco, having previously earned my MD and MPH degrees. My research focuses on the intersection of professionals and emerging technologies, drawing from the fields of medical sociology, organizational theory, and science and technology studies. I am particularly fascinated by the evolving relationship between physicians and artificial intelligence, the phenomenon of physician influencers, and the social...

Valeria Ramírez Castañeda

Data Science for Social Justice Fellow (2024-2025)
Integrative Biology

Valeria Ramírez Castañeda is a Colombian biologist currently pursuing a PhD in the Department of Integrative Biology at the University of California, Berkeley. I completed my undergraduate degree in Biology at the National University of Colombia and earned a master's degree in Ecology and Evolution, as well as another in Science Communication. During her PhD, she is studying the interactions between snakes and frogs and how this influences the evolution of toxin resistance in snakes. She is also collaborating and leading projects regarding the consequences of English in science and the...

Python Fundamentals: Parts 1-3

October 14, 2024, 5:00pm
This three-part interactive workshop series is your complete introduction to programming Python for people with little or no previous programming experience. By the end of the series, you will be able to apply your knowledge of basic principles of programming and data manipulation to a real-world social science application.

Python Data Wrangling and Manipulation with Pandas

October 10, 2024, 2:00pm
Pandas is a Python package that provides fast, flexible, and expressive data structures designed to make working with 'relational' or 'labeled' data both easy and intuitive. It enables doing practical, real world data analysis in Python. In this workshop, we'll work with example data and go through the various steps you might need to prepare data for analysis.