Jake S. Truscott

Jake S. Truscott

Assistant Professor
Dept. of Political Science
University of Florida

Transcripts of Ketanji Brown Jackson Hearing

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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.

As a disclaimer, I should note that these transcripts might have a few errors. The closed captioning software employed by C-SPAN proved to misspronounce terms (or names) or omit certain rhetoric all together – especially when the committee members were squabbling (which was fairly often). Nonetheless, it is my hope that this data serves as a placeholder until official transcripts are released by the Senate Judiciary Committee. Please feel free to use this data at your own discretion. All I ask is that you provide the necessary citation for C-SPAN and for myself having collected and sorted the transcription data.

Click Here to Download KBJ Hearings Transcripts

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An Optimally Classified Supreme Court: Teaching Optimal Classification as an Introduction to Models of Spatial Voting

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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.