Assessment Brief: Individual Policy Report
Overview
| Weighting | 30% of module grade |
| Format | Individual 4-page policy report |
| Topic | Based on your group’s sustainable energy investigation (Phase 2) |
| Submission | Committed to your personal GitHub repo by the deadline |
What you are writing
A policy report answering your group’s research question. Imagine your reader is a government adviser: busy, not a specialist, and needs to make a decision. Your report should tell them what you found, how confident they should be, and what it means.
This is individual work. Your group collaborates on data collection and analysis; you write up independently. Your report should reflect your own interpretation, your own figures, and your own conclusions — even if you used shared data and code.
Structure
Your report should be approximately 4 pages (roughly 1,500–2,000 words including figure captions, excluding code). Use the structure below:
1. Summary (half page)
The main finding, the key evidence, and your recommendation. A reader who stops here should still know the answer.
2. Introduction (half page)
The question you investigated and why it matters. What motivated the investigation? What is the policy context?
3. Methods (half to one page)
Your data sources, analytical approach, and the statistical tests you used. Enough detail that someone could reproduce your analysis from your GitHub repo.
4. Results (one to one and a half pages)
Your key figures (2–3), statistical test results, effect sizes, and confidence intervals. Interpret each result in plain language. Every figure should be referenced in the text and captioned with a “so what” — not just a description of the axes.
5. Discussion and conclusions (one page)
What the results mean and what they don’t. Acknowledge limitations, assumptions, and potential confounders. State your policy recommendation and how confident you are in it. Be honest about uncertainty — that is a strength, not a weakness.
Format guidelines
- Length: 4 pages, roughly 1,500–2,000 words. Figures count towards the page limit.
- File format:
.qmd,.Rmd,.md, or.docx— your choice. If you use Quarto or R Markdown, include the code that generates your figures. - Figures: 2–3 well-chosen figures. Quality over quantity. Each figure must be captioned and referenced in the text.
- Citations: Cite your data sources. A simple inline citation is fine (e.g., “Source: DUKES 2025 Table 5.6B”). You do not need a formal reference list, but do name your sources clearly.
What we are looking for
| Criterion | What this means |
|---|---|
| Clear research question | The report addresses a specific, answerable question |
| Appropriate methods | Statistical tests match the data and question; assumptions are checked |
| Honest presentation | Figures are not misleading; numbers have context (“Is that a big number?”) |
| Effect sizes and uncertainty | Results include confidence intervals or effect sizes, not just p-values |
| Limitations acknowledged | The report says what the analysis cannot tell us |
| Clear communication | A non-specialist could follow the argument from question to conclusion |
| Reproducibility | Methods are described clearly; code is committed and runs |
Commit history matters
Your GitHub commit history shows how your work developed over time. We expect to see commits from multiple weeks — not a single commit the night before the deadline. The commit history:
- Demonstrates your individual contribution
- Provides evidence of genuine engagement (not just AI output)
- Shows iterative development — real analysis evolves
A report with no commit history, or a single large commit, will raise questions about how it was produced.
AI use
You may use AI tools (ChatGPT, Copilot, Posit Assistant, etc.) to help with coding, structure, and editing. You must:
- Understand everything in your report. If asked, you should be able to explain any figure, test, or conclusion.
- Check AI output. AI makes confident errors. You are responsible for the accuracy of your report.
- Not use AI to generate data or fabricate results.
- Commit an AI reflections document (from Week 9) alongside your report, describing how you used AI and what you accepted or rejected.
The assessment is designed so that generic AI output is insufficient. Your report requires interpretation specific to the data your group chose and the analytical decisions you made.