This is part of a series of technical essays documenting the computational analysis that undergirds my dissertation, A Gospel of Health and Salvation. For an overview of the dissertation project, you can read the current project description at jeriwieringa.com. You can access the Jupyter notebooks on Github.
My goals in sharing the notebooks and technical essays are three-fold. First, I hope that they might prove useful to others interested in taking on similar projects. In these notebooks I describe and model how to approach a large corpus of sources in the production of historical scholarship.
Second, I am sharing them in hopes that “given enough eyeballs, all bugs are shallow.” If you encounter any bugs, if you see an alternative way to solve a problem, or if the code does not achieve the goals I have set out for it, please let me know!
Third, these notebooks make an argument for methodological transparency and for the discussion of methods as part of the scholarly argument of digital history. Often the technical work in digital history is done behind the scenes, with publications favoring the final research products, usually in article form with interesting visualizations. While there is a growing culture in digital history of releasing source code, there is little discussion of how that code was developed, why solutions were chosen, and what those solutions enable and prevent. In these notebooks I seek to engage that middle space between code and the final analysis – documenting the computational problem solving that I’ve done as part of the analysis. As these essays attest, each step in the processing of the corpus requires the researcher to make a myriad of distinctions about the worlds they seek to model, distinctions that shape the outcomes of the computational analysis and are part of the historical argument of the work.
Read More: Know Your Sources (Part 1), Extracting Text from PDFs, and Downloading Corpus Files.