How to investigate a real issue — by talking to real people, gathering real data, and weighing real evidence. Learn it once; use it in every project.
You are not writing a report from what you already know. You are finding out something true about a real issue in your community.
Every Civic Lab asks you to investigate a live, contested economic decision — and to take a position you can actually defend. This toolkit gives you the methods to do that. Every skill here transfers: once you can run an interview about food prices, you can run one about anything.
An informational interview is a conversation where you learn from someone with direct experience. It's the single most powerful research tool you have, because it gets you something no article can: a real person's reasoning, in their own words.
The difference between a question that opens someone up and one that shuts them down is whether you're leading them to an answer:
A survey gathers information from many people, so you can see patterns a single interview can't.
A field study is hands-on observation and measurement in the real world. For economic issues, the most powerful one is a price-and-access study.
Record honestly. Photograph your data sheet, note the date and time, write down anything unusual. Your data is only as good as your honesty about how you got it.
You don't need to be a statistician. You need to find a few real numbers and understand what they mean.
Not all sources are the same — and that's okay. You just have to know what you're holding.
You are living through a shift your grandparents can't imagine: getting information is now almost free. Ask an AI the right question and you'll get an answer in seconds. That's real, and you should use it.
But the shift reveals something. If everyone can get the information, the information isn't where the value is anymore. The hard parts — the parts that are now more valuable — are the human ones: knowing whether an answer is true, understanding what it means, and using it well. AI is great at the easy part and bad at the hard part.
When AI gives you a fact, ask: "How would I prove this to someone who didn't believe it?" If your only answer is "the AI said so," you don't have it yet.
Let's be honest about what AI is: a tool, like a calculator or a computer. It handles the mechanical layer — getting information, basic writing, organizing what's already known. Using it for that isn't cheating, any more than using a calculator for arithmetic is cheating. There's a lot of anxiety about AI right now because it's new, and that will fade the way it faded for the calculator and the spreadsheet. Let it. Use the tool.
But notice exactly where the tool stops being a tool.
Getting information is mechanical. Deciding what to do is not. Every real decision in this course — should the city build the store, what should we do about food access — involves tradeoffs with no right answer. Someone gains, someone loses, values collide, and a human has to weigh them. No calculation resolves it, because it isn't a math problem. It's a judgment — and a judgment is an expression of what you value.
Hold onto this for the rest of your life: decision-making is sovereign to humans. Not because machines aren't smart enough yet, but because a decision among tradeoffs is a values question, and your values are yours. Hand that to a machine — especially one owned by a distant company whose values you've never examined — and you haven't saved effort. You've given away your agency. You've let a platform you don't control decide what matters.
And that road has no prosperity at the end of it. A person — or a community — that surrenders its economic decisions to distant systems doesn't get richer or freer. It gets dependent. This entire course is about the opposite: building the judgment to make your own economic decisions, and the power to make them count.
This is where your research becomes a stance.