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Portfolio for English 606: Topics in Humanities Computing

Please Give Me Some Space! (Week 14)

Trevor Harris calls for more scholarly attention towards the use of cartography as a tool for the humanities. Harris, a professor of geography at West Virginia University, is just one of many scholars propelling the Spatial Turn in the humanities.

In “Geohumanities: engaging space and place in the humanities,” he explains that “the Spatial Turn reinserts space as an active participant in human events and behavior,” and it “calls for a critical revaluation of space and spatiality across disciplines where social theory is part of, and contingent on, a triad of space, time, and social structure.”

Harris goes on to discuss the potential of spatial storytelling for humanities study, and makes an important distinction between thin maps and deep maps. Thin maps are straightforward; their primary objective is to relay data. Deep maps, on the other hand, are “heavily narrative-based and interlace autobiography, art, folklore, stories, and map.”

While reading, I initially thought about two maps that are presented as videos and combine time and space to cause a reaction. The first map is the widely known World Population map that represents the growth of the world population from 1 A.D. to 2007, created by the Population Connection, a nonprofit group that advocates for population stabilization.

The author’s intent, as emphasized in the 1 and a half minute introduction, is to warn the public that if the population continues to grow at its current rate, Earth will exceed its carrying capacity.

The map uses one dot to represent a million people. The year is displayed in the bottom-left corner and an additional historical landmark, such as the Roman Empire, is displayed in the bottom-right corner. Additionally, there is a heartbeat noise that speeds towards the end, signifying something like the lead up to a heart attack.

Pulling from ecocriticism, this map is a clear example of apocalyptic rhetoric, and although I am critical of the map now, it caused a powerful reaction when I first watched it in high school.

The second map is the Time-Lapse of Every Nuclear Explosion Since 1945. This map, created by Japanese artist Isao Hashimoto is a colorful representation of every nuclear explosion from 1945 to 1998.

The design is reminiscent of classic video games (like Pong). The count per country is displayed in the frame surrounding the map. The month and year steadily change in the top-right corner and the total number changes in the bottom-right corner. The nuclear explosions are represented with varying colors and sounds, creating an illuminating orchestra that is discomfortingly beautiful. At the end of the video, the ‘scores’ are displayed, showing that the United States has ‘won.’

Another example, which I will demonstrate next week in my project presentation, is Esri’s Story Maps. In Harris’ “Deep geography – deep mapping: spatial storytelling and a sense of space,” he discusses technologies that can be used by humanities scholars to map stories. One example he provides is Esri’s MapsOnLine. I’m guessing this was the precursor to either Esri’s Story Maps or Esri’s ArcGIS Online.

The Story Maps feature allows you to easily create beautiful and dynamic stories using maps, text, images, videos, and audio, and I think it could be a powerful tool for humanities scholars.

Video Games: More Than Just Play (Week 13)

The readings this week were woven together with a major emphasis on processes, procedures, and expression. Furthermore, all of the readings this week advocate for more scholarly research of games, specifically the input (the code and procedures), as most existing research focuses on the output (the screen and gameplay).

In Procedural Rhetoric, Ian Bogost acknowledges that video games are not taken seriously in academia: “videogames are considered inconsequential because they are perceived to serve no cultural or social function save distraction at best, moral baseness at worst” (viii). He analyzes the procedural aspect of video games under a rhetorical lens and proposes the concept of procedural rhetoric to capture this rhetoric. He defines procedural rhetoric as “the art of persuasion through rule-based representations and interactions rather than the spoken word, writing, images, or moving pictures” (viii).

Bogost claims that, because video games are expressive, they are well suited for rhetorical speech/persuasion. Video games utilize the enthymeme: “the player performs a great deal of mental synthesis, filling the gap between subjectivity and game processes” and “a procedural model like a videogame could be seen as a system of nested enthymemes, individual procedural claims that the player literally completes through interaction” (43). The player is willing to accept the claims put forth in video games, and often this is done unknowingly.

I agree that video games deserve more attention as rhetorical objects and more “acceptance as a cultural form” (vii). The readings this week made me think critically about the different types of video games I’ve played, such as the Sims, Dance Dance Revolution (DDR), Runescape.

Relating this information to some games, like the Sims, is easy. It is more difficult with other games, like DDR. The procedures involved in DDR seem so simplistic; it’s difficult to initially see the value of studying the procedural representations of the game.

The player must step on the correct arrows as they match up with a line on the screen. The level of accuracy influences the number of points awarded and the words that appear on the screen, such as “Perfect” and “Boo.” There are no lively characters or realistic setting for the game, and there certainly is no story.

Noah Wardrip-Fruin, in Expressive Processing, uses the term expressive processing to “talk about what processes express in their design–which may not be visible to audiences,” such as their “histories, economies, and schools of thought” (4). Considering these hidden processes, such as the culture embedded in the game design, validates DDR as something that has significant cultural and social functions.

On another note, in the Introduction to Technical Communication for Games, Jennifer deWinter and Ryan Moeller discuss the technical communicator’s potential role in the production and dissemination of video games.

Although the reading did not touch upon this at all, I thought about my technical writing work at ITS. From there, I thought about the terminology of the audience. With technical writing for WVU’s IT services, the audience is the user. With video games, the audience is the player.

There may be nothing behind this distinction, but it may add to the perception of video games as not worthy of serious consideration from scholars and technical communicators.

Math for Humanists? (Week 10)

This week’s readings introduced the idea of topic modeling as a digital humanities tool. The concept of Latent Dirichlet Allocation (LDA), the primary example of topic modeling in the readings, is credited to David Blei, Andrew Ng, and Michael I. Jordan.

I felt that no one text provided a good definition of topic modeling. In “Words Alone: Dismantling Topic Models in the Humanities,” Benjamin Schmidt refers to topic models as “clustering algorithms that create groupings based on the distributional properties of words across documents.”

In the same edition of Journal of Digital Humanities, Andrew Goldstone and Ted Underwood call topic modeling a “technique that automatically identifies groups of words that tend to occur together in a large collection of documents.”

In Maryland Institute for Technology in the Humanities’ overview of topic modeling, they provide attributes of topic modeling projects as opposed to a concrete definition (their 5 elements of topic modeling projects are corpus, technique, unit of analysis, post processing, and visualization).

According to Schmidt, LDA was originally designed for data retrieval, not for exploring literary or historical corpora. And he expresses concern with the uncontextualized use of topic modeling in the digital humanities field.

He acknowledges that topics are easier to study than individual words when trying to understand a massive text corpora. However, he also expresses that “simplifying topic models for humanists who will not (and should not) study the underlying algorithms creates an enormous potential for groundless–or even misleading–insights.”

His concerns primarily stem from two assumptions that are made when using a topic modeling approach: 1) topics are coherent, and 2) topics are stable. Schmidt then proposes contextualizing the topics in the word usage/frequency of the documents.

Although Schmidt stays positive and realistic (he supports topic modeling; he just wants digital humanists to understand its limitations), the underlying point that I got from the reading is that perhaps that digital humanists are meddling in things they shouldn’t be (at least, not yet).

Schmidt hints that the people who can use topic modeling the most successfully are those who understand the algorithms, at least on a basic level. And this makes sense. That’s the reality for any tool.

This brought me back to the debates about whether or not digital humanists need to know how to code (I feel like I keep coming back to this topic). If we can’t agree that digital humanists need to know how to code, how can we agree or disagree that digital humanists need to be able to understand the algorithms of topic modeling?

The concept of topic modeling is mildly confusing, but still attainable. The algorithms, however, are straight up intimidating. The Wikipedia page for LDA shows a ton of variables and equations that would take more time and effort to understand than I am capable of giving.

Maybe if we discussed this in class, we would come to same conclusion as we did with the need to code for digital humanists: they shouldn’t have to be experts, but they should know enough to talk about it with an expert. But who are the experts in topic modeling? Statisticians, perhaps?

I think that digital humanists who wish to conduct research across a large number of texts could benefit from studying statistics. I’m starting to realize just how many hats digital humanists must (or at least should) wear!

The Last 5% (Week 9)

Fyfe’s “Electronic Errata” chapter in DITDH examines the decreasing value of correction and copy editors in scholarly publication. This has been the trend for quite some time, as seen with the elimination of the reading boy, but it has become more pronounced with the increase of online and digital publishing.

According to Fyfe, despite the importance and increasing relevancy of this topic, copy editing and fact checking are often omitted in research on digital publishing.

Fyfe’s claims about the decreasing value of correction did not surprise me. Although I do not have a great sample that I can compare current published scholarly materials against (I entered the University in 2007, and most of my academic reading consisted of textbooks and literature until I began my graduate program in 2014), I have seen many scholarly books and articles, both online and in print, with spelling and grammar errors.

Once, in a book about editing, I found a really bad mistake that seems to have resulted from copying and pasting, which is always a dangerous affair.

We seem to live in a society that is so concerned with producing, editing is often pushed to the side, and is sometimes forgotten altogether, which is somehow both concerning and relieving to me.

It’s concerning, because copy editing is a viable career option for me. Also, I believe that errors, even tiny spelling errors, are distracting and unprofessional.

When it comes to writing articles for the IT knowledge base, I tend to focus on the small details, such as making sure all uses of “drop-down” have a hyphen or that all hyperlinks open the linked website in a new tab.

‘Enforcing’ these rules is challenging when there are multiple people writing and editing articles. With thousands of articles, it’s even challenging for me to remember how I wrote a certain word or formatted a table in an earlier article. To create consistency, I created the style guide, but it is underused.

It’s important to be detail oriented, but focusing on the small things gives me less time to focus on the large things, which in the end, probably matters more. Most readers looking for information will care more about accuracy and usefulness. More importantly, readers care that information is simply available.

Sometimes with time sensitive articles, I focus less on perfect wordsmithing and grammar simply to get that information to readers as quickly as possible (I do always read through my work at least once, just not as carefully, and if I get hung up on a certain word or phrase, I leave it alone). For this reason, I am relieved that the last 5% is not as necessary as it used to be.

However, I think it also depends on the formality of the publication. I do think that scholarly publications should be held to a higher standard when it comes to copy editing and especially fact checking. I think Fyfe’s conversation about crowd sourcing copy editing is interesting, but unrealistic. I think it could work well in some academic circles if one or more of the scholars in the circle are, what one might call, grammar nazis, but I think the task of copy editing should fall mostly to the author.

As I say this, I realize that it would create a lot of extra work for authors to have to learn and apply style guides, which may change from journal to journal. But it is the author’s reputation on the line and they should be responsible for their work.

However, I also think that one or two mistakes should be forgiven, so long as the content is rich.

Describing Images with Images (Week 8)

In “How to Compare One Million Images” [UDH], Lev Manovich discusses the challenge for the DH field of accounting for the crazy amount of data that exists and continues to grow. He introduces the software studies initiative’s key method for analysis and visualization of large sets of images, video, and interactive visual media (251).

There are two parts of this approach: 1) “automatic digital image analysis that generates numerical descriptions of various visual characteristics of the images,” and 2) “visualizations that show the complete image set organized by these characteristics” (251).

His outlined approach addresses problems that DH researchers struggle with when they use traditional approaches. These include scalability, registering subtle differences, and adequately describing visual characteristics. The approach also accounts more for entropy, the degree of uncertainty in the data.

For me, this idea of entropy echoes with Johanna Drucker’s concern in “Humanities Approaches to Graphical Display” [DITDH] with the binary representations required for traditional scientific approaches to graphical displays.

I think the connection lies in the separation that Drucker describes between science’s realist approach and humanities’ constructivist approach and the need for the DH field to forge their own path in statistical displays of capta.

Note: although I agree with Drucker’s characterization of data as capta (something that is taken and constructed rather than recorded and observed), I will use the term data throughout the rest of this post for simplicity.

I think Manovich’s approach for handling large sets of data makes sense and is a viable option for the DH field, as long as they can afford the necessary computer programs and have the necessary technical expertise. As Manovich explains, a project like comparing a million manga pages (or even 10,000) would be exceptionally difficult without computer software that can measure differences between images.

For example, tagging can be problematic because even with a closed vocabulary, tags can vary. As mentioned earlier, the human eye cannot account for the subtle differences among a large number of images.

Most DH projects utilize sampling (comparing 1,000 out of 100,000 images), but sampling data can be very problematic. When sampling from a large data set, there is always the possibility that the sample will not accurately represent the entire data set. This is something that every field, both in the sciences and humanities, has to deal with.

Manovich’s scatter plots, line graphs, and image plots are beautiful and interesting and I thought they were surprisingly simple to read and understand for being so nontraditional. Describing images with images just makes sense.

Find the pattern in this response. Is there one or is it apophenia? (Week 7)

Dan Dixon’s “Analysis Tool or Research Methodology” chapter in UDH introduced the psychological phenomenon of pattern recognition in the context of DH. He explains that the DH field has an affinity towards finding patterns, but the field (and most others) have ignored “the nature of what patterns are and their statuses as an epistemological object” (192).

After very briefly explaining the psychology of pattern recognition, the systems view of the world that all pattern-based approaches take, and validating patterns as an epistemic construct, he discusses the occurrence of abductions and apophenia, and this was the section that I found most interesting. As I read about apophenia, I thought about my studies and what I’ve learned so far about the DH field, and I thought doesn’t this happen a lot?

So when I read Dixon’s conclusion, I really took note of one of the questions he posed: “Are we designing patterns where none existed in the first place and is there an unavoidable tendency towards apophenia instead of pattern recognition?” (206).

I think this is a valid and important question that might not have a straightforward answer. I think that, yes, the field does tend towards apophenia, but I think it can be avoided, or alternatively, it may even be okay. I can easily see how one could tend towards apophenia. I think it’s natural to preemptively predict an answer to a research question before research begins.

I also think that there’s pressure for professionals to validate their research within their field, and this may cause apophenia or simply slight manipulation to reach the desired outcome, such as removing certain words from a word count.

My problem with apophenia, or at least Dixon’s definition of apophenia, is with the idea of “ascribing excessive meaning” (202). How do we know when someone has ascribed excessive meaning to a perceived pattern?

Dixon does get at how we determine if a pattern is really there. He suggests that pattern recognition, by itself, is not a valid method of enquiry, and then suggests using inductive and deductive reasoning to prove abductive reasoning. Induction and especially deduction can invalidate a pattern. I agree with this, and I think that it is the researcher’s responsibility to fully account for the valid patterns that appear and be able to recognize when apophenia has occurred.

However, I also think that even loosely developed patterns that are formed from apophenia can be important (as long as it is acknowledged as such). If the researcher can create a unique and productive discussion from the barely formed pattern, it shouldn’t be cast aside.

Furthermore, a single pattern can have several different meanings, depending on the researcher, the research question, the field of study, the context, etc. What may be unimportant in one field may be important to another.

Because the main topic of this weeks readings is digital archives, I want to quickly connect the Dixon reading to the Parrika and Rice & Rice readings. Patterns play a significant role in archives. They help archivists group and organize items. They influence the way items are tagged in an archive. They influence the software and interface of the archival system. And the way that items are grouped, organized, tagged, and retrieved can force patterns that may not emerge otherwise.

Teaching XML in the Digital Humanities (Week 6)

Birnhaum, in “What is XML and why should humanities scholars care,” addresses how we should teach XML. He suggests that the Text Encoding Initiative’s, or TEI’s, Gentle introduction to XML is not gentle enough and suggests learning the syntax of XML after the introduction (although, under this stance, character entities could have been removed from this introduction).

Birnhaum’s gentle introduction was written for an undergraduate course called “Computational methods in the humanities.” The course was “designed specifically to address the knowledge and skills involved in quantitative and formal reasoning within the context of the interests and needs of students in the humanities” (taken from the class syllabus at  http://dh.obdurodon.org/description.xhtml).

In his gentle introduction, Birnhaum takes the stand that digital humanities scholars will need to learn XML at some point, and this stand is even clearer in the syllabus. How should we teach XML?

To help me explore that question, I try to relate it to how I’ve learned programming languages. How did I learn HTML? Mostly by reading online references like w3schools.com and practicing through Notepad. Every new command I read I tried to recreate on my local server. It was very skill based.

Yes, I wanted to be able to create a website, but I mostly wanted a skill to put on my resume. I didn’t think about design and functionality (other than, does the code do what it’s supposed to do). I didn’t think about why I, as an English student, should care or how HTML could be used in a context other than putting content online.

I’m currently learning Javascript through an introductory web development course on Udemy, and so far, I (and the instructor) have been focused on building a skill. I partitioned my screen to display the online reference on the left and Notepad ++ on the right. After I enter new code, I save and refresh my browser window to see if it worked.

The instructor likes to let the code’s output explain itself. He repeatedly says “this will make more sense later in the course.” Sometimes after successfully writing a section of code, I try to think of how it will be useful, and sometimes I can’t answer that.

The instructor essentially throws us in there with very little introduction, but I like that full immersion. HTML and Javascript are languages, and if immersion is an effective technique for learning French or German, why can’t it be an effective technique for learning programming languages?

It was hard for me to learn about XML from this introduction. It was especially hard to learn the terminology without seeing them in action. I actually felt like McDonough’s “XML, Interoperability and the Social Construction of Markup Languages: The Library Example” did a much better job at contextualizing the use of XML in digital humanities, even though it was specific to digital libraries.

Whether a digital humanist slowly learns XML or is thrown into the deep end probably depends on the person and the context. Regardless, I think it’s extremely beneficial to have XML (and other computer-based) classes specifically designed for digital humanists.

Those classes could fill in the gaps that, for example, occurred in my skill based learning. The classes could include discussions about XML problems in the digital humanities, such as interoperability, which is a problem that would not be as urgent to a web developer creating a website for a business.

Computational Humanists (Week 5)

For this week’s reading response, I’m going to hyper-focus on Jeannette Wing’s “Computational Thinking.”

This article was published in March 2006 in Communications of the ACM, a journal that focuses on the computing and information technology fields by covering “emerging areas of computer science, new trends in information technology, and practical applications” (http://cacm.acm.org/about-communications). 

In this short 3 page article, in what I assume is an attempt to garner student (and parent) interest in the Computer Science degree program (at that time, Wing was the President’s Professor of Computer Science in and head of the Computer Science Department at Carnegie Mellon), Wing explains the extensive benefits of computational thinking.

Under the title of “Computational Thinking,” and in blue font to stand out, she writes “It represents a universally applicable attitude and skill set everyone, not just computer scientists, would be eager to learn and use.”

As I read that sentence with only a best guess of what computational thinking is, I nodded in agreement.

Although she says repeatedly that computational thinking is for everyone, she still seems to focus on computer science. It ends up sounding more like computational thinking is mostly another, perhaps more managerial, layer for those in computer science and similar fields.

Furthermore, when she lists the post college careers for computer scientists—”medicine, law, business, politics, any type of science or engineering, and even the arts”—the use of even makes it seem like she was anticipating it would be a surprise or that it may be considered a stretch.

This isn’t surprising. Based on our readings so far, it sounds like some in the DH field and many in the traditional humanities fields may consider it a stretch to go into the computer science field after receiving, for example, an English or history degree.

Despite her representation of “the arts” as a stretch, some of Wing’s characteristics of computational thinking resonate with some of the discussions our class has had from earlier readings, especially her claim that computational thinking is “a way that humans, not computers, think.”

This goes back to the idea that computers may be able to find answers, but they don’t know the right questions to ask. That’s on us.

Additionally, her claim that computational thinking focuses on “ideas, not artifacts” may tie to the discussion of whether digital humanists need to know how to code.

As Hayles suggests in the UDH reading this week, “not every scholar in the Digital Humanities needs to be an expert programmer, but to produce high quality work, they certainly need to know how to talk to those who are programmers” (58). This suggests that the computational concepts used to solve problems are as important (probably more so in the DH field) as the actual code.

What was most surprising in this reading was her claim that “some parents only see a narrow range of job opportunities for their children who major in computer science.”

Was the field hurting that badly in 2006? I know a lot has changed in the past 10 years, but it was still surprising to read this.

Today it seems like computer science is one of the best degrees for job prospects. Afterall, as exemplified in the Manovich reading this week, software is deeply integrated into our very culture and “‘adding software to culture changes the identity of everything that a culture is made from.” This results in a lot of jobs.

Hayles asks how engagements with digital technologies change the ways humanities scholars think. To follow, are humanities scholars implementing Wing’s description of computational thinking? Is “computational thinking” even in the field’s vocabulary?

Bogost’s Reading Machine (Week 4)

This week’s readings raised a lot of questions about the connections between the humanities (especially literary and rhetoric studies), the sciences, and computer technology.

Ramsey’s Reading Machines explores the use of computer programs for text analyses in the humanities. He supports the increased use of computer technology in the humanities, but he expresses concern that the field is trying to mimic the sciences’ position with computer technology as a means to create an objective analysis. Humanists conducting text analyses must find a balance between the machine’s objectivity and the researcher’s subjectivity.

Thinking about the title as I read, I couldn’t help wonder who is the machine: the computer, the researcher, or both combined? By the end, I would say it’s both combined.

A topic in this book that particularly caught my interest was Mathew’s algorithm, a procedure designed to generate poems by “remap[ping] the data structure of a set of linguistic units (letters of words, lines of poems, paragraphs of novels) into a two-dimensional tabular array” (29).

The author shifts the characters in each row to form new words in the column, combines the new words, and this creates an unpredictable poem or story.

While reading about Mathew’s algorithm, I was reminded of Bogost’s Latour Litanizer, as described in his book Alien Phenomenology, and so I wanted to put Reading Machines in conversation with Object Oriented Ontology (OOO).

The Latour Litanizer creates a list of things (objects, people, events) by utilizing Wikipedia’s random page API.

For example, right now I’m generating a list through the Latour Litanizer (by simply clicking a button) and the product is

“The Sea Urchins, Cults: Faith, Healing and Coercion, Subhash, Roman Catholic Diocese of Limburg, Barber-Mulligan Farm, Charles Teversham, 2010-11 Belgian First Division (women’s football), Knox Presbyterian Church (Toronto), George Davidsohn.”

The list is designed to be random (at least in the confines of the algorithm, which may exclude repeats and more). Despite the randomness, I still form connections between the words. For example, Roman Catholic and Presbyterian Church (and some may argue cults) relate to religion and Limburg and Belgium are connected geographically.

On Bogost’s blog with the Latour Litany, he explains that this was created out of his curiosity of combining ontography and carpentry.

He describes ontography as “the techniques that reveal objects’ existence and relation” and carpentry as “the construction of artifacts that illustrate the perspectives of objects.”

The list puts things together than otherwise may never be linked, and we create relations from our knowledge and experiences. Therefore, the list may mean more to one person than another. Not only do we form or not form connections with the objects, they may form or not form connections with each other, although these connections are much harder to understand.

Although the Latour Litanizer seems more random that Mathew’s algorithm, both reveal new ways of read a text. They reveal connections (for example, Mathew’s algorithm revealed a prominent connection in form and the Latour Litany revealed the diversity of things humans deem worthy of having a Wikipedia page).

Whereas the Mathew’s algorithm may focus on a novel or a poem, the Latour Litanizer is constantly demonstrating new ways to read Wikipedia as a large body of text that represents society to some degree.

The Latour Litany is a unique example of a program that performs a text analysis of an entire website. It might not be the most productive exercise for researchers, but perhaps for distant reading, it could be useful for getting the bigger picture.

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