"Let Me Just Interrupt You": Estimating Gender Effects in Supreme Court Oral Arguments

This is replication code to support the paper "Let Me Just Interrupt You": Estimating Gender Effects in Supreme Court Oral Arguments which is forthcoming in the Journal of Law and Courts.

If you use this code or data, please cite our paper:

@article{cai2024interrupt,
  author    = {Cai, Erica and Gupta, Ankita and Keith, Katherine A., and O'Connor, Brendan and Rice, Douglas},
  title     = {“Let Me Just Interrupt You”: Estimating Gender Effects in Supreme Court Oral Arguments},
  journal   = {Journal of Law and Courts},
  year      = {2024},
  note      = {Forthcoming}
}

Set-up

Follow these instructions to set-up your repository. You will need to download Anaconda to run conda and python commands.

git clone git@github.com:kakeith/interruptions-supreme-court.git
cd interruptions-supreme-court
conda create -y --name scourt python==3.9
conda activate scourt
pip install -r requirements.txt

Raw Data

The raw data in the raw_data folder come from the following sources:

1. The Supreme Court Data Base.

2. ConvoKit's Supreme Court Oral Arguments Corpus (which sources from Oyez).

3. Rafo et al.'s World Gender Name Dictionary

4. To create justice-ideology.txt, we measure ideology using the average (arithmetic mean) of the justice’s time-varying Martin-Quinn score (Martin and Quinn 2002), and treat any values less than zero as a liberal justice, and values greater than zero as a conservative justice.

5. The file backchannel.txt are phrasal backchannel cues that the authors curated.

Code pipelne

Pipeline descisions are detailed in scripts/config.yaml. To replicate our code pipeline and analysis, run the following scripts in order

1. For pre-processing and chunking,

   cd scripts/ 
   python create_analyze_chunks.py
   python filter.py 
   

   This takes about 15-20 minutes to run on our machine.

2. For the main analysis and plots in our paper, run all cells in the following jupyter notebook

   scrips/analysis.ipynb
   

3. To make "Figure 5: Justice Interruption Rates (y-axis) by Martin & Quinn Ideology Scores (x-axis)", run scripts/interruptionsPlot.r using R.

4. For supplementary and corroborative analyses run

   scripts/supplemental_analysis.ipynb
   

5. To obtain the results in the Appendix for the backchannel cue removal, use the configuration specified in scripts/config-backchannels.yaml and re-run the pipeline (create_analyze_chunks.py, filter.py and analysis.ipynb).

Notes

In the ConvoKit/Ozez data there are still errors with John G. Roberts Jr. when he was an advocate. This results in warnings after running create_analyze_chunks.py such as John G. Roberts Jr. not found in caseid2stuff dict, assigning unknown. case id: 1991_90-6531. This warning should not substantively affect the results.