Archive Journal has published an essay by Meaghan Brown (Folger Shakespeare Library), Paige Morgan (University of Miami), and Jessica Otis (Carnegie Mellon University), “Identifying Early Modern Books: Challenges for Citation Practices in Book History and Early Modern Studies.”
The article considers the tools and methodologies of bibliometrics, offering insights into the study of citations to a wider variety of source material at play in the humanities and the unique challenges in automating processes for studying relationships among sources within the humanistic disciplines.
Bibliometric research and the methodology developed for it have typically focused on recent article publications in the sciences. This analysis has been more difficult to perform on humanities scholarship, due to the humanities’ heavy reliance on single-author monographs and “older sources,” by which most bibliometric studies mean secondary works more than fifteen years old. The monograph, while critical to many humanities fields, is currently a black box when it comes to studying citations, as the contents of in-copyright monographs are not commonly available as data for researchers. Humanities scholars must contend with a much broader range of sources than in the sciences, both in date and format.
The authors drew on data from five journal titles requested from JSTOR in two batches (one including citation & keyword information, the other including 7,500 full text articles) and a hand-encoded set of fifty articles from one of the journals, to develop a replicable approach to identifying citation information in each of the cases. Focusing on journals making use of the Chicago Manual of Style (or local variants thereof), the authors analyzed the three data sets to identify citations through a variety of methods. The authors’ work and discussion reveal the substantial complexity in machine recognition for the citation practices in such publications and they offer a comparison of approaches for doing so.
The authors close with a set of proposed “Best Practices for Human Comprehension and Machine Computation,” providing recommendations for a range of practitioners whose work affects or is affected by the legibility and usability of citation information in humanities texts: authors, editors and creators of style guides, data publishers, and data miners.