In Stephen Ramsay and Geoffrey Rockwell’s, article Developing Things: Notes toward an Epistemology of Building in the Digital Humanities, both authors cover topics that deal with debates happening in the field of Digital Humanities. After reading this piece, there was one moment in particular that stuck out to me. They reference the fact that a book with a bibliography can surely qualify as a scholarly work, but raise the question of whether the creation of digital tools can qualify as scholarly work as well (Building in Digital Humanities). As someone who hopes to engage with the Digital Humanities in my professional life, this spoke to me personally. If I choose to work in a primarily computational space, can my work still be considered scholarly? This question is discussed at length, and Ramsay and Rockwell believe that there is currently no definitive answer. The engaging with software can be split into two segments in this sense. There is the building process and the implementation of the product (Building in Digital Humanities). I believe that Ramsay and Rockwell’s case that focusing more on the building aspect of software can detract from the scholarly aspect of a work. Take for example neural networks. Many programmers can build them to identify handwriting. But, how many of those could use their creation to interpret a historical figure’s letters while synthesizing contemporary context with their analysis to create a new theory about some past event or culture? I believe that this is what sets a digital humanist aside from other scholars, and agree with Ramsey and Rockwell that Digital Humanists are able to create scholarly software. This quote in particular speaks to me on this issue:  “If the quality of the interventions that occur as a result of building are as interesting as those that are typically established through writing, then that activity is, for all intents and purposes, scholarship” (Building in Digital Humanities).

Tom Elliot and Sean Gillies expound on the usage of Digital Geography throughout the humanities in their article Digital Geography and Classics. Mapping geography in texts has been around since the 1990’s, but advancements in computation have allowed for the field to take off recently (Digital Geography).  This allows for an additional way to help search and share texts across disciplines. Originally, people would usually search by creator, time, or topic. With more nuanced text analysis, accurate geographical data can be extracted, helping others access it.  One prime example of improvement in this interdisciplinary field is the Pleiades Project. The Pleiades tool can be used to continuously update and diversify data sets of mapped text (Digital Geography). This allows for the digitization of these resources, increasing their availability as well. Overall, this is an exciting way to help index and share texts, and I am interested to see how it develops. As a student, I see the impact of these types of projects directly. This type of search could help me when researching for a paper or other project, as well as provide access to the text.

When programming effective pieces of software, one must always consider the idea of scale.  It may be  effective to code at a small scale in order to dissect the inner pieces of your script. When analyzing data or meta data, designing for a small scale will not be effective, as one can be working with up to millions of entries. This is a problem that Lauren Tilton, Taylor Arnold, and Courtney Rivard address in their piece Locating Place Names at Scale for the 2018 Digital Humanities Conference in Mexico City. They speak about this same challenge of scale in regards to extracting geo spatial data from meta data in order to better visualize the movement of peoples during the 20th century in the United States (Locating Place Names).  It is easier to extract this data through close reading, but this is not possible when using a large corpus.  They were able to solve this complex problem though analyzing the meta data of when people spoke about migration through their texts, collecting a list of new locations to visualize (IBID). It is interesting to see how they were able to use meta data, and a strong understanding of how to analyze it at scale in order to create a fuller and more accurate depiction of human flow.

Catherine D’Ignazio and Lauren Klein’s paper Feminist Data Visualization provides insight on how those in the computational studies can better represent their data with equity.  This paper brought together concepts, resulting in a guide for their term Feminist Data Visualization [D’Ignazio Pg 1].  One idea that stood out to me was their point on rethinking binaries. In a space where booleans are in common use (and for good reasons), it is often easy to look at the inputs / outputs of an algorithm and determine that they are either correct or incorrect. D’Ignazio and Klien state that a powerful way to make your data more feminist is by doing data collection and classification while accounting for fluid categories [Pg 2]. This allows one to cover a wider breadth of data and have accurate representation, one specific example being gender.  Some might say that this process of collection may lead to the creation of messy data. But it is important to note that computational tools are there to further sort and provide insight into that mess, allowing one to find new insights on the problem being investigated.

Matthew Jocker and Ted Underwood’s paper Text Analysis and Visualization provides a useful introduction to the methods used when working with large quantities of text. The problem proposed is simple. We live in a world with seemingly endless information; how can we eliminate the fluff and get what we want? One can analyze a corpus (body of text(s)) to search for the ubiquity of a single word, or even a pattern of characters. These results can reveal similarities and/or correlations within the corpus that can be useful when researching and annotating works. One could even create a new text solely based upon the analysis. This allows for powerful visualization, resulting in artistic and insightful interpretations of data. Text analysis and visualization is a powerful supplementary research tool that should not be overlooked.

Laura Klein exposed me to a new view on social networks through her tracking of the relationships and interactions that occurred in Thomas Jefferson’s household. The main example she uses to jump start the paper is one of Thomas Jefferson’s slaves and personal chef: James Hemings. It is easier to track the relationships between Jefferson and other politicians or social elites due to documentation of events and access to education. However, one must also consider that there was networking happening in the lower classes as well. Through documents passed between Hemings and other slaves/workers, valuable relational data has been found and added as metadata to a variety of documents. Klein stressed that looking at the top level and creating a clear diagram of relationships is possible, but not necessarily true to history. Accounting for all interactions (especially those of slaves) reveals that the Jefferson household was a complex network, helping reveal the true nature of many people’s lives, including who they relied on and/or trusted the most.

Jeffrey Binder’s piece about text mining had a few points that stood out to me. These were in the discussion on Topic Modeling, its applications as a literary tool, and its common criticisms. By analyzing the different words included in digital documents, one is able to discern the topics that are discussed inside. What is impressive about these programs to me is that they are able to extract the text and then create new ones for further analysis. However, one must consider what documents they are mining from. Are the works creative or quantitative? Are they trustworthy? The data collected must be interpreted to a standard of source type. Having a better grasp on computational ideas allows one to better mine and interpret the resulting text. One must understand that a computer’s standard for language is directly related to the code it runs.

Chapter four of Isaac Asimovs “The Psychohistorians”, contains a discussion of modeling that is applicable to the creation and reception of current models. The character Seldon reveals to his new acquaintance Gaal that Gaal is being followed. This is due to both of their involvement with the usage of a type of model, called psychohistory, that predicts the collapse of their dystopian government. The most important take away is that Seldon recognizes the power of his model, the value of Gaal’s (though its error is decently large), and the implications of the data yielded by both. Modelers must be conscious of the type of data and parameters they are using, so they may be conscious of their outputs. In Seldon’s case, their conclusions clash with the government’s hope to keep the public in the dark. A parallel in our world could be the models of climate change. The scientists in our world have warned us of the repercussions for ignoring their model’s finding, but many still chose to ignore them due to conflicts of interest.

Welcome to WordPress. This is your first post. Edit or delete it, then start blogging!