2,260 participatory processes. Which designs leave a mark?
Python, logistic regression, JavaScript
Participation Design Advisor
A tool for anyone planning a citizens' assembly, participatory budget or public consultation. You describe the design (who takes part, how much power they get, how decisions are made, whether it will be evaluated) and it estimates the chance the process documents real impact, ranks the changes that would raise it, and lists the closest past cases. It learns from 2,260 Participedia cases in 124 countries, plus 356 cases added to Participedia since. Planning a formal evaluation is the strongest lever (+11 points), yet only 15% of processes do it.
Method and limits
A regularized logistic regression (AUC 0.91 on held-out cases) scores each design in the browser. Every effect holds documentation fixed, because thin entries rarely record impact whatever happened. A context check shows how common a recruitment method is in countries like yours, from the companion research paper (under review). The repository also holds that paper's case classifier, a CatBoost and XGBoost ensemble that sorts the 2,260 cases into five method families (76% accuracy against baselines near 52%).
Documented impact is not proof of impact, and the archive is crowd-sourced. Associations, not causes. Not an official Participedia product.