While writing about my data science course at the School of Information in the spring of 2016, I realized that I needed a long preface to explain why it was that a historian of Vietnam was using computational methods in their research. My long engagement with the world of ‘tech’ has become less of a dabbling and more of a blurry (exciting) amalgamation where all of my work in history, digital humanities, quantitative methods, data science, and information science have converged.
However, when I mention my work and interests in quantitative methods and computer science, I often end up confusing others. (A few generalizations, based on real life examples to follow.)
When I speak to non-humanist tech folk, they often want to hear the programming languages and specific projects I have worked on. And then they ask me why I am doing this ‘tech’ thing while doing a Ph.D. in history.
When I speak to humanists/historians, they often do not want to hear about my ‘tech’ work. But when I start explaining it anyway, they want to know 1) What is the question I am answering and 2) How can I answer it any differently than ‘traditional’ methods of historical analysis and close archival research. And then they ask me why I am doing this ‘tech’ thing and imply that it is in fact distracting me from doing ‘real work’ for my Ph.D. in history.
I realize that some of this confusion is because I (like most people, actually), do not fit neatly in a box.
I am a historian of Southeast Asia, and am deeply informed by quantitative methods and scientific reasoning. I am interested in the colonial and post-colonial history of Vietnamese libraries in the twentieth century. I do not just do ‘techie’ things on the side, or have a ‘digital humanities component’ to my Ph.D. dissertation in history. The world of digital humanities, experimental design, and quantitative methods is deeply interwoven into my thinking and research.All of these seemingly disparate fields have converged and push me to investigate my methods of questioning, explanation, and discovery.
Conversional Research – Methods of Questioning, Explanation, and Discovery
From experimental design, I continue to work through what comprises evidence, argument, causation, and validity. (See “Validity” in Krippendorff, Klaus. Content Analysis: An Introduction to Its Methodology. SAGE, 2004) Unlike the over-simplified understanding of science, I find ‘answers’ that are nowhere near the ‘truth.’ Rather, most of my answers are probabilistic—the quantification of uncertainty. This process of experiment and results offer ‘answers’ that gesture and converge towards my research question. Most importantly, they offer new insights into my sources (aka data) that I would not have considered before.
From data science, I hone down my research questions to think through what is operational as well as innovative for my work. The interpretability of these methods to a larger audience is important to me, especially as I seek to make my research process more transparent.
From history of science, I learn about the human and social production of ‘objectivity’ and the historical development of ‘trust in numbers.’ Furthermore, history of science and history of the book remind me to pay attention to material production and the human actors involved in fine-grained technical decisions.
All in all, my humanistic training teaches me the importance of closely scrutinizing source materials, research methods, and data production–something that is at the foundation of all of my research.
A Bibliography and Roadmap of my ‘Tech’ Training
In the field of intellectual history, in order to understand the intellectual upbringing of an individual we investigate the educational background and social and intellectual networks of the individual. I attempted to lay out my own intellectual roadmap of my ‘tech’ training and projects I had completed, as well as the ‘concrete skills’ I had sampled:
- Cultural Heritage Informatics Summer Institute — Digital humanities survey, Geospatial Information Systems (GIS), Webscraping, Open Refine and data cleaning, Web design, Data visualization
- Vietnam Project MSU archives – Digitization, Library science, Metadata, GIS
—> I began to self-identity as a ‘digital humanist.’ At this point, I had completed work on a digitization project in Michigan (2012), moved to the SF bay area (2013), and found myself working as the ‘digital humanities assistant’ to the new Digital Humanities at Berkeley program. (2013-2014)
- Berkeley Digital Humanities Working Group – Infrastructure building, Webscraping, Online presence
- Umea Digital History Course – Digital humanities survey, something about network analysis…
- Digital Humanities Summer Institute, Victoria – GIS
- Digital Humanities Summer Institute, UC Berkeley – Network Analysis, Data cleaning
- Digital Humanities Summer Institute, Victoria – History of the book, Linoleum relief printing
- Vietnamese Intellectual Networks Database – Drupal, Data cleaning, SQL
- Digital Humanities Summer Institute, UC Berkeley – 20% of a Text analysis class
- Qualifying Exams in the fields of Data Science, Digital Humanities, and History of Science
- Training Workshops
- D-Lab, UC Berkeley – Python, R, GIS, Zotero, Qualitative data analysis, Drupal
- Software Carpentry – Python
- Stamen – GIS
- Digital History graduate seminar – Survey DH projects
- History of Data Science – Critical historical analysis of field
- Deconstructing Data Science – Python, Experimental design, RegEx
How useful is this exercise? For me, making this bibliography of my educational background and the skills I sampled reveals a few things:
- I have done a lot of things.
- No history of intellectual development is a simple, linear one.
- There is a lot of redundancy in my training as well as fruitful diversions. How many survey classes of digital humanities does it take to fully ‘comprehend’ a field that is constantly changing? Why is it important for a digital humanist to work on materiality as well?
- And finally: A list of ‘skills’ and training background does not reveal as much as about my conversional research between the seemingly disparate disciplines of data science, history of the book, history of science, and statistics.
My intellectual roadmap has brought me from critical theory and source criticism to programming languages and experimental design. This interdisciplinary journey of cross pollination has pushed my research to new horizons. Rather than get lost in the semantic battle of defining disciplines (What is/are the digital humanities?), I just want to make stuff.
I want to learn new things, test them out, struggle a bit outside of my comfort zone, and figure out if it was all worth it in the end.
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