Designing Criticism


As I read the piece by Ted Underwood and Jordan Sellers, an article that our composition reading group read slowly began entering my mind (very slowly…more like as I skimmed over my notes from reading…that is more accurate).It was an article by James Purdy called “What can Design Thinking Offer Writing Studies?” that tried to show how a concept called “design thinking” has historically infiltrated composition and rhetoric. Many of the moves made throughout the Underwood and Sellers piece reminded me of the kinds of qualities that Purdy claims that design thinking has: thinking of the future more than past problems; focusing on combination and connection more than critique; generating many diverse solutions (emphasis on quantity before quality) (Purdy 620 and 626).

This is not a perfect analogy to lay onto DH scholarship (e.g., there’s not quite a series of “users” that Underwood and Sellers are trying to tailor their product for in the same way that designers do…in some ways there arguably are, but I think it might be some kind of hybridity that lays between critique and making that makes this analogy imperfect [making, at least, in the sense of designing] ), but I think, broadly speaking, the emphasis on the future and the emphasis on testing things out and living in method more than argument aligns nicely with the kind of work that designers perform.

Much like a hypothesis, Underwood and Sellers begin by thinking in terms of possibility and the future:


This is the problem, but the problem leaves itself open for many solutions. Underwood and Sellers are only leaving it open for something “interesting” by examining “changing characteristics.” As we see throughout the rest of the piece, there are many ways to slice up these changing characteristics. There are definitely considerations of quality throughout this slicing, but it is left very open-ended and we are left with a surplus—an emphasis on quantity—of methods of using and interpretations of data throughout the piece.

After the first chart, and the subsequent teasing out of the surge in use of words that entered the lexicon pre-1150 in literary genres, they write the below with a new chart:


The picture becomes more nuanced with poetry rising, prose fiction rising (though, less so than poetry), and nonfiction declining. Underwood and Sellers further complicate this picture by slicing the data along more specific genres. They describe the issue with drama and then start to discuss the problem of viewing too absolute and certain a distinction in change of language between literary texts and nonfiction texts because of the heterogeneity of genres. Going away from a previous argument (or “solution”), this leads to looking at a new dataset in a new way here:


After debriefing this chart, and how it shows the pitfalls of wondering too much about the similarities and dissimilarities between fiction and nonfiction over time, Underwood and Sellers build a new question (more…quantity rather than quality…exploring possibilities): “What was concretely entailed in the formation of a specialized literary language?”

After surveying some other works, historical and critical, about what made poetic diction become specialized, Underwood and Sellers’ next move is to answer another distant reading done by Heuser and Le-Khac that concluded that this shift was due to a decline in words associated with abstract values and an increase in concrete words. Rather than dismiss the study outright, they use this work to suggest that this work “overlaps in great part with” their own claims. Because of this overlapping, Underwood and Sellers decide to do more digging, going beyond the aggregate of the uptick of pre-1150 English words in 19th century literature and look to see individual words and their correlation to the variations in the diction ratio. First looking at the positive correlations followed by the negative correlations, some thematics are drawn from both groups (more concrete words, and some more abstract-ish experience words; fewer words connoting social relationships). Nothing is taken as hard proof here, and Underwood and Sellers continue to undercut their claims, prompting others to possibly investigate this data more. No demystifying here, just trying to encourage looking at the data in increasingly new ways to try to find things unseen before.

The (seemingly honest and not rhetorical) gesture toward the continuance of research is especially apparent in the last paragraph of the main essay and in the “Supporting Data and Code” section. They write that “this is a collective enterprise” and go on to acknowledge friends and critics as crucial to its making—it’s noteworthy that this is not in a separate section called “acknowledgements” but is in the main body of the text. The closing sections is filled with hyperlinks and even advice about how to filter collections of poetry to make sure the prose sections are not captured into the data. This is almost a gesture of: here’s how we did it, you try it, too. Or, make it even better. Constant invention, constant brainstorming, constant thought for the future, combination, connection. Very design-like.

Purdy, in the article on design thinking that I cited earlier, concludes his piece by writing that design thinking situates the goals of writing studies to “describe, explain, and enact the gamut of writing practices and products rather than to judge (or to dismiss) them” (632). With Underwood and Sellers, and it might be safe to say about the majority of DH scholarship, there’s a larger focus on descriptive work that is continuous and collaborative in the kind of spirit of design thinking that Purdy puts forth. There’s less critique and more testing; there’s more quantity and experiments than a Sisyphean task of finding the “best”, in kind of an Arnoldian way. Underwood and Sellers open their article by exploring what literary criticism once was: a definitive and descriptive practice. It might interesting to go back and read the New Critics and the Russian Formalists and to see if they might be designers in their own right, which might be more in degree than kind.

Ready to do some stuff

I’m excited about this workshop later, but I plan on doing a distant reading project for this class, so, yeah. I have my .csv file, and even figured out how to combine the 1000 .csv files into a single file. I had to use the command prompt, which is a big deal for me that I used it well (even if it took me way longer than it should). Now, I have to just figure out something to do with all of this information.

I have never done a distant reading before, though I took Steve Carr’s class and learned a bunch about it. I’m glad I’m going to actually try to do something this time around. So, I guess I’ve sort of gotten to know distant reading in a way that I know Marxist criticism or something like that. Yet, I do think distant reading is kind of a hybrid of a method and a sort of a theoretical lens. Sort of like it’s a school of theory but also kind of a nuts and bolts approach to reading at the same time. A weird sort of hybrid. But maybe all theories are sorts of hybrids like this? I guess there could be a case for that. Maybe it is a matter of degree or kind.

Anyway, Ramsay’s take on algorithmic criticism is, for me, a convincing reason why it is kind of both a method and a theoretical lens—which I find directly relatable to many of the conversations we’ve had as far as what computers do as well as how they might make us see the world differently: “If algorithmic criticism is to have a central hermeneutical tenet, it is this: that the narrowing constraints of computational logic…is fully compatible with the goals of criticism set forth above….such procedures can be made to conform to the methodological project of inventio without transforming the nature of computation or limiting the rhetorical range of critical inquiry. This is possible because critical reading practices already contain elements of the algorithmic” (16). These constraints are methodological, but the claim that critical reading practices “already contain elements of the algorithmic,” I think is a claim of how to view reality similar to how we have discussed previously that programming is world-making or how customer service was procedural for Bogost. I like this approach, and it feels like many of these readings we’ve done have had these sorts of tennis matches between computers as tools and as aesthetic or theoretical objects in themselves.

I’ll take a dwarf shed or outhouse at this point

So, my experience has been very similar to Amiga’s. Much of my time was spent figuring out how to actually play the game (literally, how to physically push buttons and do things) and also reading up on some dwarf profiles and what not. The tutorials from the wiki helped me at first. I could figure out a decent place to settle (though I decided later this area sucked) and then I just got completely sidetracked on how to actually do any damn mining. Was it happening? Shouldn’t there be some kind of walls being set up at some point? I unpaused the game for several minutes and nothing seemed to be happening. But I was getting nervous. My dwarves needed me and I did not want my incompetency to lead to their demise. So like any good political leader, I relied on some good old fashioned stasis in my decision making. I spent many moments yelling at the wiki to be more clear about how to get these dwarves to work. The wiki, I suppose, does not respond well to this kind of treatment. It returned no answer. That’s my fault. I apologized, but still, the cold shoulder is what I received.

Simula’s link to this was really helpful. Things began to, at least, make a little more sense to me because it is loaded with screenshots. Still, no mining was happening. I did note that much of my land was covered in snow, and thought, well, maybe it’s just too cold to mine. Or something. So, despite my earlier devotion to my beloved dwarves’ welfare, I abandoned them to their fate and created a new world to hopefully pick a better spot, figure out how to make a fortress so I could say I fully experienced both words in the title of the game, and start from scratch with Simula’s link instead of the wiki.

Ok, so things are making a little more sense. There’s trees, and plants, and not a bunch of white wavy lines (i.e., snow) all over the place. I want to make the fortress, but the wordpress person wants me to gather plants, so maybe that matters (I guess stuff to make the fortress?). I’m ready to gather some plants as the wordpress person tells me. I’m trying to designate an area for this to happen. No dice. I try again, and I think I get it. You have to hit enter in kind of a diagonal fashion? I’m not sure. Either way, something happened. Shapes changed over after hitting enter for designation in this diagonal move. I unpaused, some dwarf moved around. I think something happened?  I just can’t say for sure. I’m also not sure how much plant gathering should go down before doing something else. Arbitrarily, I decide to pause and move on.

I’m down to the end of my Dwarf Fortress endurance, and now that I am feeling slightly more confident, why not try to make the fortress again? I designate just like the plant gathering only this time I select “mining.” But nothing seems to be happening. Why does the game refuse to report to you what the hell is going on? Who am I? Aren’t I a dwarf deity of some kind? If so, why am I not omniscient? I certainly seem to have some kind of great power, maybe not quite omnipotent. I certainly feel blind. I can see the little smiley faces moving around, but as far as what they’re doing, I have no clue. I also am pretty sure that zero mining is being done.

But hope enters (only to then quickly exit). Aha! Something. An announcement. What great wisdom to you bestow upon me oh mighty Dwarf Fortress? What’s that? You say, “It is now raining.” How insightful. Does that mean mining can’t be done in the rain? Like some part of a collective bargaining agreement between my dwarves and me? Have they unionized? I’ll bust their union you just wait!! Twelve hour work days for all; I won’t budge on it! Well, no fortress for me yet. Hopefully by the next go of it, some sort of dwelling will take shape, even if it’s some sort of dilapidated mess.

Mr. Roboto only wants you to make the grade

Ian Bogost’s opening move in the first chapter of Persuasive Games,  “Procedural Rhetoric”, was to appeal to a game called Tenure. I was both surprised and pleased to see that teaching was positioned as a game so prominently in a book about games. As a reminder since this already feels like a while ago, Bogost’s description of Tenure accounted for a training device that was meant to simulate the first year of teaching with the aim toward getting a contract for the second year (1). Through the multiple choice decision making events, the procedural argument about teaching is that it requires a complexity of decision making that a teacher must navigate, involving contradictory and multidimensional conditions related to classroom management, collegiality with other teachers, student advocacy, and career advancement (to name a few) that can have dynamic results, many of which may or may not be in the best interest of any one party at any one moment. Bogost hones in on the aspect of institutional politics of the game, but I’d argue that even within the confines of determining what is the best decision to make as a teacher with a duty toward students, complexity runs abound in any decision made.

Where there is complexity, there is also, I am increasingly finding, a beckoning for potential assistance from computation. If we can explore the possibility of mechanical ethicists, as Maher does in “Artificial Rhetorical Agents and the Computing of Phronesis,” then why not mechanical teachers? Teaching, after all, can be rule-bound and is also heavily invested in making decisions. In a way, it is a series of moments of dealing with oughts and ought nots, much like what Tenure procedurally displays. Maher’s description of Beliefs, Desires, and Intentions as creating an architecture that “attends to the fact that action most often occurs in a space of ‘competing alternatives’ that must be weighed before deciding one” suggests that the machine’s potential ability to deliberate about a moral decision is similar to the Tenure player’s deliberation about whether or not to ignore a student’s tardiness (15). But let’s imagine what could happen if we remove the player and insert the machine as player. What would it be like to have a robot teacher?

random robot with 2 plus 2

Apparently, from Ohio to South Korea, robot teachers are already in action. However, these robots are controlled by humans and not (at least totally) by code. I’m interested in something like Maher’s AMA (which would require an ARA). I guess, to be very creative, we will call it the Artificial Teacher Agent (ATA) for now. If we accept that teachers deal with oughts, then perhaps it wouldn’t be too much of a stretch to go from moral systems to teaching decisions. After getting through the promise and problematics of Kantian and utilitarian ethics as models for machines to run, Maher spends a moment on Aristotelian virtue ethics and case-based ethics, providing an example of Marcello Guarini’s method that “uses a series of cases to train artificial ‘neural network models’ so that any kind of abstract moral rules rise organically from the situation rather than deontologically” (10). The machine would be educated in morals through this process of experiencing case-based ethics of sorts. If we maintain an acceptance of the assumption that teaching can be considered a series of oughts, we might also consider the dynamism and contingency that the situatedness of teaching provides. Things that maybe can be planned for, but perhaps still can’t be completely planned for (situations that might somehow violate rules and models we hold).

Some Teaching Oughts  that I thought of (this is furious free association, so it could be a little suspect)

This student ought to spend time doing this kind of writing


I ought to spend more time on working on the understanding of the assignment in class with students


I ought to grant an extension because the student has been dealing with a personal issue


I ought to reach out this student to have a meeting about generating ideas for writing


I ought to be more stern today to send a message that the class or assignment will be difficult


I ought to be more friendly today to send a message that this is a space for experimentation


I ought to say very little today and do more listening to see where they are at



I read a book this summer called After Pedagogy: The Experience of Teaching by Paul Lynch. Lynch’s project is a response to a movement in composition studies called “postpedagogy.” A brief (and probably sloppy) attempt to define this movement would be something like this: work in the field has positioned developments in pedagogical theory as an impossible aim because, as Lynch writes, teaching is “too complex, too particular, too situated to be rendered in any repeatable and therefore portable way” (xiv). In other words, pedagogy cannot be relied upon, as a priori, in order to reliably make decisions (and good ones) as teachers. Lynch’s response to the movement is not a rebuttal or a counterargument, but instead, drawing from John Dewey’s conception of experience (one that is very difficult to pin down), a move to develop a philosophy of experience in teaching writing that accounts for both the “raw data of everyday living” as well as “our methods of reflecting, repurposing, and learning from everyday living”, so that compositionists can make a sort of yin and a yang of pedagogy and contingency (xix).

One of Lynch’s heavy investments for his application of a philosophy of experience to teaching is in the moral system of casuistry.  Essentially, casuistry “asks whether and when circumstances change the ways in which we judge moral action….casuists check their judgments against paradigm and analogy and frame their decisions for the particular case at hand….a judgment holds for only for the given case” (Lynch 104-105). For an application to classroom ethics, Lynch provides the following example:

“A student asks for an extension. ‘I am swamped in my other classes,’ she says. This claim (which students invariably fail to understand insults the teacher to whom they are talking) might not win much sympathy, until we consider that the student (a) has never missed a deadline, (b) has been stellar all semester long, (c) is holding down a full-time job, (d) is raising three kids alone, and (e) is a member of the honor society. Given these circumstances, teachers might be inclined to bend the rules a bit. This is a basic casuistic situation, in which circumstances seem to demand some deviation from the usual procedure (111-112).”

There is so much information available and imagined at any given moment in the classroom and in preparing for class. If we imagine an ATA like an AMA, then, how great would it be (and possibly this would be a requirement like for the AMA) if it was also an ARA. If it also had to explain the decisions it made in the classroom. Lynch’s example is complex, but the classroom certainly has more potential for complexity. The words I choose as a teacher, the activities I design, the way I arrange the room, the questions I ask, the way I speak to students at any given moment, how I talk to one student vs. another student (say, a student with low self-esteem and another with, well, plenty of esteem to go around) : all of these are decisions made for one reason or another, and they may lead to myriad outcomes. Because of the situatedness of teaching, I might have no time or foresight to prepare to make some of these decisions (or adjustments to decisions already made). If an ATA might have some sort of case-based learning ability, it might make some interesting and effective moves as a teacher.

Perhaps it wouldn’t work, but maybe, like Tenure, there could at least be some training value. Maher also seems to retreat to this sort of possibility about the potential to learn about morality and possibly update it for a digitized world (30). Teaching teachers, via intentional professionalization or more routine observations by administrators or other teachers, has long been held as a difficult and onerous task, especially when implications for one’s career are implicitly or explicitly involved. Maybe having a machine report out decisions it would make in your classroom could  be an outstanding way to consider something like Lynch’s argument for a balance between pedagogy and contingency: considering and reflecting on what we’ve done to learn how to (loosely) teach tomorrow (only to again follow the same pattern). Having an ATA (that is also, necessarily, an ARA) explain its decisions to a teacher (novice or seasoned) might be promising for future professionalization and training of teachers. This would ostensibly remove the interpersonal anxiety between and among teachers that sometimes manifests itself; the lack of a holistic subject that one has an ongoing social relationship (or, at least, could have an ongoing social relationship with) might allow for someone to let their guard down when considering the case-analysis by the ATA. This might kind of be like the Rogerian ELIZA that found itself to be a great conversation partner. Rather than right or wrong answers, the ATA could provide options in decision making that the teacher may not have considered and that information might inform future decisions (pedagogically a priori or ad libbed in the moment of teaching in the future).

Or it could be a terrible intervention that only encourages neoliberal surveillance and quests for “teacher effectiveness”. I could also see this being terrible. But, I think it is interesting to imagine as a useful possibility.

Excited but disappointed but also excited

Learning about Python and Twitterbots was both incredibly exciting and incredibly disappointing. Exciting I’ll get to, but since I’m from the Northeast, the more negative feeling usually surfaces first so I’ll start there.

Now, Matt and the workshop were both fantastic; it was a great job all around. What I mean by disappointing is that there was just SO MUCH I did not know and also did not know that I did not know. I mean, the Python training was pretty hard but I thought I had some handle (ableit fairly small) after doing it. After seeing some of the code (and what might be behind that code), though, and especially after seeing the possibilities of code and the errors that spit back and the ways to interpret the errors and the ways to search for solutions to errors and the eventual befuddlement at what any of those solutions searched for produced and the myriad available libraries and functions that I had difficulty parsing through for building a bot and all of the words that I thought were familiar to me but had no idea what they mean when used in regard to programming (initiating and returning and run and execute)…it’s all kind of exhausting and a tad disheartening.

Still, the sheer amount of possibilities such work holds is really cool. I’m not sure what I might do with this potentiality I have in front of me, but I was able to see how it worked (even if to a small degree) which helped me see some semblance of interesting things that could be an outcome from wielding code. Maybe I’m picking up on some of the procedural expressiveness inherent in such tools by physically doing some of this work in both Codecademy and in the workshop, which is very different from reading Bogost and saying “Yeah, I see that procedures can make arguments and expressions.” Doing it makes it real. And possible. Just don’t know a good how or why of that possible quite yet. I want to do some cool thing related to a research interest I have, but it hasn’t quite come to me yet. It’s out there, somewhere, though. Just need to go find it.

Despite my excitement/disappointment duality, there are things I can think about that were only very hazily apparent to me before I dug into Python and Twitter bots. That’s exciting. And probably exciting only because of this disappointment I seem to be expressing.

Mptyeay Eepskay Rintingpay when it shouldn’t!!

The PygLatin module is driving me nuts. See my first crack, the one that makes the most sense to me based on the module’s instructions:




I didn’t enter a word, so the fact that “empty” appears in the console checks out. Now, here’s when I do enter a word, which should NOT print “empty.”




Still get empty for some reason. I’ve noticed that when I type in original = raw_input(“Enter a word:”) in line 5, that I do end up getting the entered word printed:




Finally, the word entered is printed and empty is not printed. But “Enter a word:” must occur twice in the console for me to reach this outcome. I moved on for a little to the .isalpha string method, but remain unsatisfied with how this stuff went so far. The “Hint” box in codeacademy was pretty generic, so it wasn’t much help. I checked out some of the forums, and played around with a few things, but it seemed like most questions just didn’t quite look the same as mine. I must be missing something really obvious that I am currently blinded to for the moment, like that sneaky comma splice or the way I used to spell separate as “seperate”. As is written in my word entry in the images above: “ugh.”

Now that my rant is out of the way, I can now confidently (somewhat) talk the talk after walking the walk of getting frustrated with writing code. Overall, the first few modules were kind of fun in the same way that learning your first few phrases in another language are kind of fun. It’s sort of “magical” in that you are accessing this source of knowledge-making that you previously found inaccessible. It’s like opening the “black box” that Mateas writes about when discussing how new media scholars were limiting themselves by not knowing how to program; now that the box is opened–even if only a crack–talking about programming feels a bit more natural, real, exciting.

Also like learning a new language, frustration sets in when applying (or trying to apply) something that just falls flat. In this example above, I kept falling flat. When I tried to order at a Dunkin Donuts in Spain, I got my coffee, but it was unsatisfying. It took way longer and much more cognitive effort (and shortcuts) than I would have liked. Similarly, with this exercise working with Python, I got the result I wanted to move forward (“done enough“), but it’s not the way it “should” look (and I don’t mean “perfect,” but rather, more acceptable or conventional, or maybe, more “precise” is the way to put it). Plus, all the time it took meant I didn’t even get to the actual pig Latin part of the PygLatin exercise, which is disappointing on another level. But really, I look at all the time I spent trying to figure things out in the PygLatin module as what, optimistically, must be a habit of mind that is useful in programming–tinkering and messing around, searching for answers from others, etc.

My analogy to language learning is nothing new, but I’ll use it to think through the relevance of programming to English studies. Just like a good language user can learn about her native language by taking lessons in second and third (and more) languages, spending some time with programming must provide some kind of insight into using a language (what? not sure yet, but I believe it’s there; I can feel it but not really see it quite yet), and more specifically, composing and reading. Programming is a “making” just like writing is a making, and it takes interpretation just as reading does. Plus, going back to the opening of the black box, even if the opening is by someone fairly ignorant (me), it must reveal something about using the medium of the computer to write and read. We love to talk about medium in the humanities, and the computer is certainly the most dominant medium for reading and writing today. How that medium is built and how it performs must offer something surprising and rather insightful about the nature of language and how language appears in the space of the computer and across the internet.