Photo by Mick Haupt on Unsplash
From Baseball Cards to Government Documents: Unlocking Cross-Jurisdictional Insights with ChatGPT
When I was younger, I had a baseball card collection that I put on display at a library. Although my favorite sport was baseball, my collection wasn’t just baseball cards. I had basketball cards, comic books, and sports memorabilia mixed in. What people liked most about my collection was its diversity—it wasn’t just one sport or era, but rather a mix of perspectives, players, and historical moments.
That reminds me of the collections at UC Berkeley’s Institute of Governmental Studies Library (IGSL). Their collection isn’t just one type of document—it includes government reports, policy papers, planning documents, and historical records from different jurisdictions, time periods, and policy areas. Just like my baseball card collection, each document represents a different lens through which we can examine history, governance, and societal change.
The Power of Multi-Jurisdictional Comparisons
One of the most valuable aspects of IGSL’s collection is its ability to bridge different cities, policies, and time periods. When analyzing government records, simply looking at one city’s housing policy or one county’s emergency response plan doesn’t always reveal the full picture. However, when you compare multiple jurisdictions side by side, new insights emerge.
For example, let’s say you’re researching urban development policies. Instead of just looking at how Los Angeles handled zoning laws, you could:
🔄 Compare policies across cities – Look at how San Francisco, Oakland, and San Diego approached the same issue.
📊 Track historical changes – See how zoning laws evolved over decades or political administrations.
📚 Contrast government priorities – Identify which jurisdictions prioritized affordable housing, commercial development, or environmental concerns.
ChatGPT can take these multi-jurisdictional comparisons and analyze trends, highlight differences, and generate summaries that would otherwise take hours to compile manually.
Using ChatGPT to Uncover Hidden Patterns in the IGSL Collection
Just like organizing a diverse collection of baseball cards, structuring the IGSL’s collection in ChatGPT makes it easier to see connections between documents, policies, and decisions.
For example, if you’re researching transportation policy in California, you could:
🚊 Compare public transit reports from different cities to see which factors contributed to increased ridership.
🌆 Analyze city planning documents to understand how freeway expansions impacted neighborhoods.
📖 Look at historical government publications to see how transit policies shifted over time.
By feeding these documents into ChatGPT and asking the right prompts, you can simulate decision-making, analyze policy effectiveness, and even predict future trends based on historical patterns.
Final Thoughts
Just like my baseball card collection allowed people to see the bigger picture of sports history, the IGSL’s government document collection allows us to see the bigger picture of governance and policy across time and place.
By using ChatGPT to compare jurisdictions, analyze policies, and track historical trends, we can unlock insights that were previously buried in thousands of pages of reports and records. The key? Knowing how to structure the data and ask the right questions—just like curating a well-organized collection.
My name is Nick, and I enjoy teaching and speaking about the intersection of research, ChatGPT, and prompt engineering. My work focuses on developing easy-to-use frameworks and strategies that ensure AI doesn’t just generate answers, but also verifies and checks itself—helping researchers use ChatGPT more effectively and responsibly. If you have questions, need help setting up, or want to improve your prompts, feel free to reach out—I’d love to help!