DH Reflection 3: Uses of Computational Analysis in REL

From Data Catalog on Flickr

For the last reflection before our final research assignment, my Digital Humanities in REL course was asked to re-evaluate what we’ve learned throughout the semester. We focused especially on what it means to ‘do data’ and what that might result in for scholars and students and research participants and everything and everyone in between. Because the concept was a bit broad, we tried to narrow our focus with a working definition of Digital Humanities and together came up with this definition:

Digital humanities combine technology with theory. Working in digital humanities requires the recognition of human error and contribution to what seems “given” when using technological interfaces present everywhere. We must critically examine the digital world, just as we analyze literature, by leaving room for humanistic contribution and not completely trusting what appears at face value. We complicate the “givens” of computational methods because knowledge production is a political act.

Dr. Wieringa’s DH in REL Class, Fall 2020.

While far from perfect, you can get the gist of what we think it means to do data in the digital humanities and in religious studies more specifically. For me and my classmates, it was important to point out that knowledge does not exist on its own, but in a context that is situated and dependent on the knowledge producer.

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DH Lab 12: Text Analysis

Text analysis has been a popular form of computational analysis since it’s inception. Whether you support close reading, distant reading, or a healthy mixture of both, there is always something to be learned when evaluating, comparing, and considering the words used by scholars, authors, poets, and anyone in between. 

Voyant Tools is a popular online source for analyzing digital texts. Any user can upload their word source and then play with the various visualizations offered by the site. All of the visualizations show the various relationships between the digitized words and can be connected and presented in unique ways. The image above shows the first page Voyant shows after analyzing the American Medical Association (AMA) Journal of Ethics, July 2018 edition. The screenshot shows the page exactly as it first appeared. I did not make any edits or refine any key-terms. This is why abbreviations like ‘dr’ are visible. 

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DH Reflection 2: Data in Religious Studies

In my first semester of graduate school, I took Debates in Method and Theory with Dr. Russell McCutcheon. In the second half of the course, we read Constructing “Data” in Religious Studies, which was (at the time) the most recent addition to the NAASR Working Papers series. If you have time to deep dive into what it means to ‘do data’ in Religious Studies, then this collection of papers is a must-read. Data is broken into the subcategories: Subjects, Objects, Scholars, and Institutions. Each scholar takes a step back to reconsider the ways that data is constructed and not discovered. 

In Digital Humanities in REL, which I am currently taking, we were asked to reflect on what counts as data for the study of religion. It kind of feels like cheating to bring in a powerhouse source like Constructing “Data” in Religious Studies, but then again, it would be just plain wrong to neglect it. Data — as I have repeated endlessly in other blog posts and in almost every class discussion — does not speak for itself, and beyond that, data does not exist by itself. This is why these subcategories of Data can exist. Social actors employ tools (like subjects, objects, scholars, and institutions) to construct data. 

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DH Lab 6: Assessing Data

We’ve somehow made it to mid-semester already. And while the workload certainly supports that observation, the time itself has flown by. Getting halfway through an upper-level course often means the focus shifts towards a final project, which is exactly where my Digital Humanities course is headed.

For our lab this week, the class was asked to evaluate a data set that might be used as a source for our final projects. The goal of the project is to formulate an argument based on the comparisons of two different datasets. One of the datasets must be the Longitudinal Religious Congregations and Membership File discussed in a previous blog post. The other source can be one of our choosing. Which is great unless you have a brain like mine that basically runs like an internet browser with too many tabs open. I’ve found one rabbit hole after another and (as often happens) have been slightly derailed from my long-term goal. This is where small goals become especially handy; as this blog post will hopefully help move me in a step closer towards finalizing my ideas for my final project.

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