# Workflow 04 — Sources and evidence

**When:** weeks 3–8, running alongside your other work. **Feeds:** the implementation milestone and the final submission.

## Goal

Find the **literature, data, and evidence** your project needs, **verify all of it**, and link your main claims to something that supports them.

## You produce

1. **A source list.** For each source: the citation, one line on what it does *for your project*, and a check mark once you've verified it.

   | Citation | What it does for my project | Verified |
   |---|---|---|
   | Author (Year), *Title* | e.g. "the study I'm replicating on a new case" | ✓ |

2. **Your main claims and their evidence.** For your 2–3 main claims: what supports each one, and what would count against it.

   | Claim | What supports it | What would count against it |
   |---|---|---|

## Steps

1. **With AI.** Map the search. AI is good at finding leads, but it **isn't a source**, and it **invents citations**.

   ```
   I am doing an undergraduate project on [question]. My core concepts are [A, B, C].
   1. Suggest search terms for each concept, plus adjacent literatures or disciplines I might be missing.
   2. Suggest kinds of public data or documents that could answer my question
      (government records, legislative text, court filings, company disclosures, research datasets, archived pages).
   3. If you mention specific papers or datasets, say clearly that I must verify each one exists.
   ```

2. **You.** Search. Start with the course readings for the relevant week, which are already vetted. Then use Google Scholar: find 2–3 highly relevant papers, and use **"Cited by"** to find newer work that builds on them. This is the most productive search move there is.
3. **You.** Verify every source before it goes on your list: it exists, it's cited correctly, and it actually says what you use it for. This includes every source an AI suggested.
4. **You decide.** Write your 2–3 main claims, and for each one, what supports it and what would count against it. Do this *before* you see your results.
5. **With AI.** Check the list and the claims:

   ```
   Here is my source list and my main claims with their evidence.
   1. For each source, ask me what it does for MY project; flag any I only summarize.
   2. Point out claims with no evidence, evidence that supports no claim,
      and claims where I haven't said what would count against them.
   3. Suggest what kind of source is missing (theory, a similar study, a method, data).
   Do not invent references.
   ```

## Check before moving on

- [ ] Every source on your list has been verified: it exists, it's cited correctly, and it says what you use it for.
- [ ] Your list covers more than background: at least one argument you build on, one similar study or method, and your data.
- [ ] Each main claim has evidence, and you've written down what would count against it.

## Common traps

- **Citing a source the AI made up.** This is the fastest way to lose credibility, and it's covered by the course AI policy.
- **Citing AI as evidence.** An AI answer isn't evidence about politics or policy, unless AI outputs are what your project studies.
- **Book-report reading.** Knowing what a source *says* isn't enough. You need to know what it *does* for your argument.
- **Confirmation-only evidence.** If nothing could count against your claim, you're illustrating it, not testing it.
- **Uploading sensitive material.** We suggest you don't paste private, identifying, confidential, or classmates' unpublished material into AI tools (a suggested practice; see `docs/polisci158-ai-use-and-disclosure.md`).

---
*Binding AI rules are the POLISCI 158 syllabus's "Generative AI" policy.*