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Below are a small collection of graphics illustrating unique features of the online political discourse that emerged in the wake of a leaked draft opinion in Dobbs v. Jackson, which would effectively overrule the Court’s landmark opinion in Roe v. Wade (1973). A few notes:
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Attached are transcriptions for the recent Supreme Court confirmation hearing of Judge Ketanji Brown Jackson (D.C. Circuit), which are available in (.rdata) format. It was constructed using C-SPAN’s (rough) closed captioning transcriptions of the hearing’s proceedings, which I was able to access by referencing the relevant hyperlink for each portion of the hearing and converting it to a plain text (.txt) file.
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Below is a bar chart illustrating the propensity for Supreme Court justices to join majority opinion coalitions during their tenures between 1791 and 2020. Each iteration represents the number of times a justice joined a majority and ends with their termination from the Court. The second video illustrates the same concept but focuses on the propensity for justices to serve as majority opinion authors. Each were created using Python (bar_chart_race) and replication materials can be provided on request.
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This work provides an overview of optimal classification, a rank-ordered scaling procedure that can be used for voters in any parliamentary voting environment. Originally developed by Keith Poole as a computational approach to scaling members of the US Congress (see Spatial Models of Parliamentary Voting 2005), I translate the approach to scaling justices of the Supreme Court. At its core, optimal classification in a single dimension uses a cutting point procedure and a legislative procedure to optimally rank order voters (though it can theoretically be applied to more than one dimension). The benefits of the approach are found largely in its ease of implementation. Unlike statistical approaches (e.g., NOMINATE or Martin-Quinn Scores), the procedure can be completed with pen and paper if the number of votes and voters is small enough. However, using a computational approach (as opposed to statistical) means that the rank ordering will not produce cardinal distances - merely an optimal rank ordering. I ultimately address how the procedure can serve as a good tool for introducing students to models of spatial voting.