Monthly Archives: March 2012

“This secret with no mystery”: The Impossibility of Mastery in the Information Age

After a particularly disheartening programming exercise that was part of an attempt to self-teach myself coding, I stumbled upon this Derrida quote from Paper Machine. It’s from a 1996 interview with La Quinzaine Littéraire, in which Derrida spoke about the experience of composing on a word processor:

I know how to make it work (more or less) but I don’t know how it works. […] Not knowing, in this case, is a distinctive trait, one that does not apply with pens or with typewriters either. With pens and typewriters, you think you know how it works, how “it responds.” Whereas with computers, even if people know how to use them up to a point, they rarely know, intuitively and without thinking—at any rate, don’t know—how the internal demon of the apparatus operates. What rules it obeys. This secret with no mystery frequently marks our dependence in relation to many instruments of technology. We know how to use them and what they are for, without knowing what goes on with them, in them, on their side; and this might give us plenty to think about with regard to our relationship with technology today—to the historical newness of this experience. (Paper Machine 23)

I think that part of what is so daunting—and potentially disheartening—about cultivating technical knowledge and skills for digital humanities work is this ever-increasing realization of your own ignorance, of the degree to which your interaction with technology continues to involve “this secret with no mystery” whose existence you’d all but forgotten.

For me, Derrida’s quote served as a much-needed reminder that this is not a situation that we’re really capable of studying our way out of. One can, of course, master the mysteries “under the hood” of a word processor; however, technology has developed at such a rapid rate—and technical knowledge has become so specialized—that the possibility of knowing every programming language, of completely understanding the inner-workings of every component, of “mastering” every element of computing, is increasingly out of reach even for computer scientists. Of course, computers are only one facet of the vast realm of technology that we interact with on a daily basis. It would seem that, in the information age, being a true “renaissance (wo)man” is no longer within the sphere of possibility.

As I struggle to carve out my little space of technical knowledge, I’m finding it useful to remember that this praxis is not only about acquiring skill; it’s also about coming up against a growing realization of how my interactions with information—my interaction with my own research and scholarship even—are mediated by technologies that are so complex and so rapidly changing that “mastery” is, at best, only a relative concept. Thinking about what it means to do scholarship in an age that no longer permits traditional “mastery” is, perhaps, a question that must inform any theorizing of DH.

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Generative Metrics: Could Distant Reading Include Distant Scansion?

Having recently read portions of Nigel Fabb’s Language and Literary Structure, I was struck by how remarkably algorithmic his grid method for determining meter is. (A useful summary of the Grid Theory used by Fabb can be found in this document from UPenn.) Theoretically, there seems little reason why this “algorithm” couldn’t be converted into a proper, computer-executable one. The uses of such a program would seem to be manifold. With the ability to “distant scan” hundreds, if not hundreds of thousands, of poems, one could chart historical shifts in metrical form or even trace the evolution of metrical usage in the corpus of an individual poet. For my own work, the ability to quickly chart metrical variation between versions of The Prelude would be quite useful.

Software has been developed by linguists to test various notions of generative grammar: most notably the Maxent Grammar Tool developed by Bruce Hayes and Colin Wilson. Yet, to the best of my understanding (and I confess that my knowledge of linguistics is extremely superficial), the Maxent Grammar Tool’s usage in generative metrics seems to be largely related to stylistics. For instance, Hayes, Wilson, and Anne Shisko used a modified version of the software to generate a Shakespeare and Milton “grammar” to challenge the importance of the Stress Maximum Constraint in generative metrics, a constraint which essentially states that only the placement of stress maximums (strongly stressed syllables bordered on either side by syllables with relatively less stress) matters in determining meter. It would seem that such software might, indeed, be able to do the type of distant reading I imagine, though its ultimate, complex purposes create a potentially unscaleable learning curve for literary scholars without relatively intensive linguistics training. Doubtlessly, a more accessible program with more limited capabilities could be built.

TEI-encoding, of course, allows for the marking of meter, but it is tempting to imagine how computer-aided scansion could greatly increase the reach of projects that aim to database poetry based on elements of poetic form. Google’s recent work in trying to get computers to translate poetry from one language to another while preserving rhyme and meter would also suggest that distant reading of metrics and other poetic elements should be possible. Indeed, the possibility seems so likely that it is difficult to imagine that a program hasn’t already been produced. Do you know of software—developed or in development—that would be be able to analyze poetry for meter on a large scale? What uses would you find for such a program?

Of MOOCs and men…

Today’s New York Times ran an enthusiastic story on the use of MOOCs (Massive Open Online Courses) by Stanford. In addition to Stanford, both Berkeley and the University of Michigan are also currently offering MOOCs through Coursera. A step further than most of the free online content provided through iTunes U and other venues, MOOCs are geared specifically to online students. Courses occur over a set period of time, complete with readings, lectures, and tests, and forums are provided for student interaction and even for limited Q&As with instructors. Undoubtedly, MOOCs highlight the democratizing potential of the digitization of higher education. But their presence also raises two important questions:

(1) Do MOOCs mark a significant departure from earlier free higher educational content available online?

(2) Where—if anywhere—would the humanities figure in this wide world of MOOCs?

Curious to see the interface, I enrolled in “Model Thinking,” a course provided by the University of Michigan. While it is doubtless too early for me to fully address the nature of this online classroom, my first impression involves pleasant surprise at the level of organization and navigability (there’s a clear integration of syllabus, lecture videos, course readings, and tests) and an immediate association with that bastion of autodidacticism, Khan Academy. Of course, “Model Thinking” can offer both institutional validity and a higher-education focus, two aspects where Sal Khan’s labor of love falls short. However, the association does suggest that MOOCs may be less “classroom-like” than they initially appear; I have difficulty imagining the experience of navigating through an archived version of the course being substantively different from “taking” the course during the window of its actual offering. After all, with literally tens of thousands of students, how much “classroom feeling”—between student and instructor and between student and student—can you actually replicate?

While losing some of the “classroom feeling” may not be a great sacrifice for science and engineering courses that often already operate with huge class sizes, it would pose a greater problem to the humanities where, for lack of a better term, the “human” in humanities education matters in a much more integral way. Perhaps because of this problem, the majority of MOOCs offered are in science and engineering (with a heavy, and unsurprising, emphasis on computer science).

Still, the idea is intriguing: is there a way to democratize humanities education? Or do the simple facts of how humanities pedagogy functions—through discussion and essay writing—force it to operate largely within institutional confines that necessarily exclude a large percentage of potential students? To return to the Khan Academy example, the site’s recent alliance with SmartHistory, an online Art History textbook combined with short videotaped conversations about important works of art, wisely avoids the quiz-framework that dominates the rest of the site. The move seems to indicate that the creators of SmartHistory recognize that computer-gradable quizzes are a really crappy way of demonstrating meaningful knowledge of the humanities beyond simple factual knowledge.

It would seem, then, that the only way that humanities education could be meaningfully delivered to thousands (or tens of thousands of people) would be through the use of hundreds if not thousands of instructors. The logistical issues of organizing such a collaboration aside, would there even be enough (already underpaid) humanities faculty and grad students willing to volunteer their time to responding to papers written by students with whom they have no direct connection?

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