Portfolio for English 606: Topics in Humanities Computing



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.


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” ( 

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?

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