Stemwijzer

A Dutch parliamentary voting compass that lets you vote on real Tweede Kamer motions and see which parties match your positions.

What is Stemwijzer?

Stemwijzer ingests motions and voting records from the Dutch House of Representatives (Tweede Kamer), stores them in DuckDB, generates AI-powered explanations with an LLM, and presents a Streamlit UI where users can vote on real motions and explore party positions through SVD visualizations, trajectory analysis, and embedding-based similarity search.

Features

• Voting Compass — Vote on real parliamentary motions and see which parties align with your choices
• Explorer — Interactive SVD visualizations, party trajectories over time, motion browser, and semantic search
• Analytics — SVD decomposition of voting patterns, UMAP projections, clustering, and drift analysis
• LLM Enrichment — Automatic generation of layman-friendly motion explanations using QWEN via OpenRouter
• Overton Window Analysis — Quantitative analysis of whether the Dutch parliamentary center has shifted rightward, using centrist voting support, SVD spatial drift, 2D extremity scoring, and mechanism classification

Prerequisites

• Python >= 3.13
• uv for dependency management
• (Optional) OPENROUTER_API_KEY for LLM enrichment

Quickstart

# Clone and enter the repository
git clone <your-gitea-url>/sgeboers/stemwijzer.git
cd stemwijzer

# Install dependencies
uv sync

# Run the Streamlit app
uv run streamlit run Home.py

# Run the data pipeline (fetch motions, compute embeddings, etc.)
uv run python pipeline/run_pipeline.py

# Run tests
uv run pytest tests/ -q

The app will be available at http://localhost:8501.

Project Structure

├── app.py              # Streamlit UI entrypoint
├── database.py         # DuckDB schema and queries
├── api_client.py       # Tweede Kamer OData API client
├── explorer.py         # Explorer page with SVD visualizations
├── pipeline/           # Data ingestion and analysis pipelines
├── analysis/           # SVD, clustering, trajectory, right-wing motion analysis
├── tests/              # pytest test suite
├── docs/               # Documentation, research, and plans
└── data/motions.db     # DuckDB database (~18 GB)

Documentation

• ARCHITECTURE.md — Comprehensive architecture overview, tech stack, and contributor guidance
• CODE_STYLE.md — Coding conventions, naming, typing, and testing standards
• docs/solutions/ — Documented solutions to past bugs and best practices

Research

• Overton Window Article — Interactive article: "Has the Dutch Overton window shifted?" with Plotly charts (render with quarto render)
• Overton Synthesis — Detailed synthesis of all indicators and the "acceptance through moderation" verdict
• Overton Reports — 13 appendix reports covering breakpoint analysis, SVD drift, 2D extremity, mechanisms, and more (reading guide)
• Overton Dashboard — Standalone HTML report with gravity-controlled charts and example motions

Tech Stack

• Language: Python 3.13+
• Data: DuckDB via ibis-framework
• UI: Streamlit + Plotly
• ML/Analysis: scipy, scikit-learn, umap-learn
• LLM: QWEN via OpenRouter (OpenAI-compatible)
• Package Manager: uv

License

[Your license here]