Should a foreign language requirement for a literary studies PhD be fulfillable by a machine language? Or maybe even by a methods course (like a course in statistics, GIS, or some other competence in computational technology)?
These questions have been on my mind since I flew back Sunday morning from an energizing time at lovely (since it reminds me of high school) UVA, where I participated in the NEH-funded Institute for Enabling Geospatial Research in the Humanities, run out of UVA’s impressive Scholar’s Lab. It was a great time, I appreciate the NEH for tossing the cheese the Scholar’s Lab’s way, and I especially appreciate all the hard work the UVA people did to pull off a seamless little three-day event.
Over the next few days (read: weeks), I hope to write up more about the various things that went on at UVA, but the one that has been needling me most came from a quick flash of Twitter conversation on the topics in the lede, prompted by Brian (@briancroxall), who wondered whether GIS methodologies should count toward a “methods” course requirement in a PhD program, as it already does at Penn’s History Department. This then led toward the suggestion that, perhaps, proficiency in a machine language should be accepted as fulfillment of a foreign language requirement in a PhD program. Ryan (@ryancordell) even wished he had spent the time cramming for a French exam learning Ruby, instead.
As I mentioned while recusing myself, I’m kind of a militant about foreign languages (the more the better), to the point where I would consider basic Spanish proficiency a requirement of any specialist of American (U.S.) literature.
But after thinking about it, I realized that the two languages, human and machine, are, bizarrely, and in most mainstream use scenarios, totally non-comparable. Human language proficiency, at the literature PhD level at least, is measured in ability to read.1 The mere fact that my classmates can (and have) fulfilled their requirements by taking “Reading German” or Latin (dead language!) courses demonstrates that the key skill being taught is reading.
Someday being able to read a machine language, however, is never the goal of learning it. As Matt Kirschenbaum writes in his appeal to humanities students to learn to program (my emph):
Many of us in the humanities miss the extent to which programming is a creative and generative activity… Programming is about choices and constraints, and about how you choose to model some select slice of the world around you in the formal environment of a computer. This idea of modeling is vital.
From this promising beginning, Kirschenbaum comes out in favor of using machine languages as substitutes for human languages in fulfilling requirements.2 If one is studying contemporary American literature, in which code can appear, he argues, it has certain value.
But Kirschenbaum’s understanding of the value of the foreign language requirement seems misplaced.3 In the beginning of his article he criticizes those who view what we literary scholars do as little more than correcting spelling and grammar in comparison to those who think wrongly that computer scientists do nothing but fix bugs in code. So why, then, is he willing to imagine the foreign language requirement in such reductive terms when he says explains that (my emph),
Knowledge of a foreign language is desirable so that a scholar does not have to rely exclusively on existing translations and so that the accuracy of others’ translations can be scrutinized. One also learns something about the idiosyncrasies of the English language in the process.
Really? That’s it? Nothing about alterity, about imagining different conceptual schemes (pace Davidson), about forcing a disruption in one’s comfort zones (and comfort Weltanschauungs)? I should learn French just so I can take Massumi to task on how he continues the tradition of the translation of “agencement”? That seems a bit… thin.
Furthermore, when he pushes human and machine languages together, since knowing code helps one understand novels in which code appears, he is working against the very point of programming with which he opens his piece: programming is world-making. Sure, if I know C, I can read a novel that has pages of C. But I’m interpreting it and re-generating there, not generating tout court, as I would be with a “Hello World” program.
So the confusion in the Kirschenbaum piece gets reflected in the argument on Twitter: a willingness to compare human and machine languages, perhaps only since they are both called “languages,” emerges. This gets amplified, then, in the academic world; looking back at Penn’s history requirements, we see that they treat foreign language acquisition as a “competence” in a “technical area” (if I read the guideline correctly), akin to competence in GIS or statistics. Language is foregrounded–the technical option seems available more to US history scholars–but it is still treated as part of the same piece.
But history is not literary study; in an English department, I maintain, knowing French and python are completely different beasts serving two different masters in the scholarly process. The former relates to how stuff goes into the scholar (reading), while the latter relates to what comes out of the scholar (analysis). Exceptions are obvious, as one could publish in French and analysis usually involves quite a bit of feedback looping, but I think that I’m more or less right for the huge majority of cases.
So, on the one hand, I do wish that I could get the sense that computational methods training had greater appeal in the humanities.4 But, on the other, I do not think that it is entirely appropriate to view methods and foreign language as either/or objects in a course of study, when, as at my university, a student needs to show only proficiency in a single foreign language.
Given how my colleagues dismissively look back on their foreign language requirement (something like “I took ‘Reading German’ and remember none of it” is common), this distinction is probably unnecessary. In fact, as far as real-world skills are concerned, a much more lucrative future can be built up in a quarter-long course on Ruby than in the first quarter of first-year French. Human languages require care and attention, like Gabriel Conroy’s going to the continent to “keep in touch with the languages.” But though machine languages also benefit from practice (like any skill), it strikes me that it’s much easier to get back in the coding swing.
Still, maybe the question gets a bit more provocative if we consider the language requirement not at the PhD level, but, rather, at the undergrad level. Undergrads at my university can fulfill their math requirement by taking intro level CS courses that include vocational programming courses for web development, but the math requirement is certainly not the same thing as the foreign language requirement, which can be met in a dizzying array of ways. I cannot imagine that my university, which has made a huge deal over the past decade about expanding study abroad opportunities, would start accepting perl proficiency as meeting the foreign language requirement. And I’m not sure that’s bad.
Now it seems like we’re still comparing two things, human and machine languages, that can only really be compared in coarse ways, like through simple, macro-level questions of time management or speculation regarding future employability–two things, I think, that fall out of consideration at the PhD level (ha!).
So back to Kirschenbaum as I try to wrap up this wandering. HASTAC recently published a response to his article that reminds readers that one shouldn’t think that CS familiarity is demonstrated by machine language proficiency. This is certainly true: I know how to build and manage a GIS, but I don’t think I’m at all a geographer. I can code, but I’m not a computer scientist. And I don’t think my two years of formal French language study make me a scholar of French.
The article continues to discuss NLP, suggesting that the humanistic disciplines and CS have quite a lot in common. This is certainly true, too: my old CS roommate and I were often working on similar projects from different angles. But that then cuts out the final leg from the table that is Kirschenbaum’s argument. In describing programming as world-making, Kirschenbaum compares the coder to Jane Austen, a formidable world-maker herself of reasonable renown. Yet CSers working on NLP aren’t making worlds. They are investigating the world around us, just like us boring literary scholars.
- I fulfilled the requirement with Russian by showing that I was reading Russian literature and engaging in literary discussion of the texts without translation. [↩]
- He hedges, hardcore, by arguing that, in fact, it should be not only case-by-case but implicitly appropriate only for programs that require two foreign languages, which is not, as I mention, the case at my university, where one foreign language suffices. [↩]
- I am working under a bit of a fantasy here about what the foreign language requirement should do for PhD students. I know the reality is just about always different. [↩]
- My committee certainly had no issues with my pursuing further foreign language acquisition, or learning statistics, or taking a full year’s worth of GIS training. The promise to learn and work with GISes was written into my dissertation proposal, even! [↩]
Tags: assemblage, Brian Croxall, Brian Massumi, Deleuze and Guattari, digital humanities, geography, Geoinst, GIS, HASTAC, language, Matthew Kirschenbaum, natural language processing, programming, Ryan Cordell, the dead