# fsQCA — African Fintech Funding Configurations
A structured, auditable pipeline for running a fuzzy-set Qualitative Comparative
Analysis (fsQCA) study end to end: from raw evidence gathering on individual
startups, through calibration and analysis, to a written paper draft.
**Research question.** What configurations of founder characteristics and network
ties are associated with higher seed-stage funding among African fintech startups
(2015–2024)?
This repository is designed to be operated by an automated agent, one task at a
time, with a human reviewer approving at three gates. Every fact the study rests
on is stored as a structured record with a source pointer, so the final paper is
fully traceable back to evidence.
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## How to operate this repo (read this first, agent)
1. **Never invent data.** If you cannot find a fact after a genuine search, record
it as `status: not_found, value: null`. A missing value is a valid, useful
result. A guessed value silently corrupts the entire analysis. This is the
single most important rule in the repository.
2. **One task at a time.** Work the task list in `PROGRESS.md` top to bottom. Each
task has defined input files and exactly one output file. Do not start a task
whose inputs are not yet `done`.
3. **Stay inside your stage.** Evidence-gathering tasks (C1–C4) search the web and
write facts. The coding task (C5) may *only* read what C1–C4 already wrote — it
never searches fresh. This guarantees every score traces to recorded evidence.
4. **Update your own cell.** When you finish a task, set its cell in `PROGRESS.md`
to ✅ and commit. Use one commit per completed task, message format:
`[case-id] Cxx: short description` (e.g. `flutterwave C2: funding rounds`).
5. **Stop at gates.** Do not cross a 🔴 GATE row in `PROGRESS.md`. A human sets
those. If you reach one, stop and summarise what is ready for review.
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## Repository map
```
.
├── README.md You are here. Agent operating manual.
├── STUDY_DEFINI …