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A statistical analysis replicating key findings from "Why Civil Resistance Works" by Erica Chenoweth and Maria J. Stephan.

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📊 Exploring Why Civil Resistance Works

Python 3.8+ Jupyter Notebook License: MIT

A statistical analysis replicating key findings from "Why Civil Resistance Works" by Erica Chenoweth and Maria J. Stephan, investigating the effectiveness of nonviolent versus violent campaigns.

✨ Features

  1. Historical Trends Analysis

    • Frequency analysis of nonviolent vs violent campaigns by decade
    • Success rate comparison between campaign types
    • Visualization of campaign trends over time
  2. Participation Analysis

    • Investigation of the 3.5% rule
    • Analysis of largest resistance campaigns (1946-2014)
    • Statistical validation of participation thresholds
  3. Statistical Modeling

    • Logistic regression analysis of campaign success factors
    • Control for population size and regime type
    • Confidence interval analysis for success probability

🛠️ Installation

  1. Clone the repository:
git clone https://github.com/marsidmali/Exploring-Why-Civil-Resistance-Works.git
cd Exploring-Why-Civil-Resistance-Works
  1. Create and activate a virtual environment:
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
  1. Install dependencies:
pip install -r requirements.txt

📁 Project Structure

Exploring-Why-Civil-Resistance-Works/
│
├── data/                # Dataset files
│   ├── NAVCO 1.2 Updated.xlsx
│   └── p5v2018.xls
│
├── notebooks/          # Jupyter notebooks
│   └── Exploring-Why-Civil-Resistance-Works.ipynb
│
├── requirements.txt    # Dependencies
└── README.md          # Documentation

🚀 Usage

  1. Launch Jupyter Notebook:
jupyter notebook
  1. Open Exploring-Why-Civil-Resistance-Works.ipynb
  2. Run all cells to perform the analysis

📊 Data Sources

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

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A statistical analysis replicating key findings from "Why Civil Resistance Works" by Erica Chenoweth and Maria J. Stephan.

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