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So I presented a prototype of my mapping project yesterday at Publishing Makerspace, a project supported by the Scholarly Communications Institute and housed here in FHI. Using “backcasting,” a technique much like Grant Wiggins’s “backward design,” after I presented, participants were invited to brainstorm about potential final outcomes for the project. Then, we worked backwards from final goal to intermediary steps. If you zoom in to the images, you’ll find some sample recommendations. They were split into five categories: “Publishing Format,” “New Uses of Data,” “Data Collection,” Outcomes–Pedagogical,” and ever-popular “other.” But what great recommendations! I thought was a great approach to the genre of the works-in-progress presentation.
Tracking the spatial movements of our characters continues to be revealing. If Newland measures out his life in city blocks, Ellen Olenska moves around and through the cloistered neighborhoods of the old New York elite. Of all the characters in the novel, she may be the most mobile; when the novel begins, she is recently returned from Nice. In the course of the novel, she moves from the “funny little house” on West 23rd Street to Washington, DC; she ends the novel independent and alone, in her Paris apartment. Despite the strictures on female mobility, women may be more mobile than men–and indeed they spur male mobilty as Newland rushes to Florida to urge to hasten their marriage; later takes the night train to Boston on the hint that Ellen is there, and plans a clandestine trip to Washington in the hopes that they will run away together. Cherchez la femme–Ellen is the big pink circle just above Newland’s polygon.
Plateaus v Quagmires: Quick Notes on Surface Learning, Deep Learning, and the Power of a Good Night’s Sleep
Last night I was about ready to throw laptop, Mac, and all against the wall…and today I was able to continue mapping, slowly but surely! Yesterday’s frustration made me think about plateaus in learning. Just as we all know the joy of accomplishment, we all know what it’s like to reach a plateau–to look around and experience the feeling that we’ve come just as far as we’ve can, and don’t feel able to go further. But that’s different from being stuck: instead, I decided to spend my plateau time enjoying the view and thinking about what I’ve learned so far.
This also recalls Ken Bain’s What the Best College Teachers Do. Bain distinguishes between the strategic learning many of our students, and all of us have engaged in at some point–learning for the test, learning proficiently, yet not going deeper. (So your Italian disappears after your trip; you can’t stick a handstand anymore after yoga teacher training; and OMG, what happened to your math–sound familiar?). In contrast, deep learning is lasting, interrogative, and exploratory–it takes longer. The benefit is depth, the trade-off is time. I’ve learned so much so quickly in the past two months and have been exposed to so many new things (I use the passive deliberately here) that I want to guard against the superficiality that can accompany strategic learning.
“A little learning is a dangerous thing,
Drink deeply, or taste not the Pierian spring.”
We all know the feeling of accomplishment when something works, whether getting up into Wheel Pose in yoga (and gaining the knowledge that you can do so regularly) OR making something work in your scholarship or on your computer that hasn’t before. I’ve been enjoying exploring the ideas that come along with this mapping project, but I’m frustrated at the moment by challenges around the execution. But as with wheel pose, it took me over a year to get there regularly and now it’s one of my favorite things to do. So “step away from the computer, Meredith; step away from the computer,” but don’t forget to come back.
Hannah Jacob’s How to Solve a Problem, Digital Humanities Edition is comforting today. And if those dancing penguins can figure it out, surely I’ll get there eventually.
Reflections on Mapping Project, Part Two:
Having roughly finished the first stage of this project (data collection), I’ve had a number of observations…
My first observation has to do with the kind of reading I’ve been engaging in for this project. The close reading I do as a literary scholar is about complicating and ambiguity in a good way: taking words on the page, with implications that seem obvious, and looking for what’s not on the surface. Taking a literary text and harvesting data from it required a different way of thinking about literary information. I spent a lot of time thinking and re-thinking metadata categories, refining them, breaking them down further. I wondered what can and can’t be quantified, like taste. I spent much more time listing and categorizing than I may have ever done while working on literature. It’s been tedious at times, but a useful exercise (I’m considering having students break down motifs in a poem into a spreadsheet now, just bc it prevents us from willfully ignoring details that we don’t know how to deal with). I also found a little bit of joy when I was able to tie the text to fact: when I found that the hotels Wharton described still exist (and could be located via coordinates) or the street address for the Boston club where Archer passes the time. Or that I could make a good guess where Archer is sitting in the final scene, as he watches the sun set over the dome of Mansart in Paris. I am a contextual close reader, but I found that working toward visualization allowed me to visualize the text—personally, emotionally–in some ways that I never have before. This gave me a sense of engagement with the text AND empowerment over the material (the technical aspect of the work) that felt new. (For a great discussion of close, distant, and hyperreading, see Katherine Hayles, How We Think.)
On a more practical note, I’m starting to understand the importance of keying the tool to the project. I only knew about one approach to mapping before I came here. But now, I’ve realized that much like literary theory (in itself, a tool)—you don’t want just one. All these different mapping programs apply different approaches to the problem, and will yield different results. Another big lesson: be persistent. I stuck with OpenRefine until I got it, if in a rudimentary way. Oh, and the Mac! I thought I would have a hard time adjusting and didn’t. It feels like something that does something for you and less like a small typewriter. That’s probably because I’m asking it to do very different things than I usually ask of my PC.
Re the nature of the work, I’m struck by the importance of collaboration/distribution of labor. I think I didn’t understand the term “workflow” until now. In traditional scholarship, the workflow is much clearer bc it’s individual labor that fits into an established pattern—read, write, research, rinse, repeat. Here, there are just so many more people involved, and I’m an expert in only one area. I’m so grateful for Will, Liz, and Brian—and that they have an understanding of literature and pedagogy. It’s made me think a lot about the division of labor as a concept—how much do I need to know about the “other side”—the technicalities of tool choices, hacking my data, mapping, writing code—in order to produce a good final product? At the moment, being basically cogent feels like enough. Is this is a place in which a clear division of labor benefits? That’s a challenge for humanists, I think—we’re all used to flying solo. (And in my editorial work, my experience in production is a huge benefit; but here, the spheres are too different; there’s just too much to leapfrog over. I think I’m starting to get that real interdisciplinarity—as opposed to lit people dipping into history or psychonanalysis or whatever—requires collaboration.)
ISome larger goals as I move forward: pre-committing to the process of trial and error as we choose the tool and begin to hack around in it. I’m sure that I’ll be re-visiting data and clarifying goals and I know I need to do some more reading and writing to frame this appropriately. Ultimately, I hope to test with students and/or colleagues. And always, I need to make sure the “so what” is clearly in mind.
I’m Meredith Goldsmith and welcome to my WordPress site. I’m a scholar and teacher of late nineteenth-century American literature. I teach at Ursinus College, a small liberal arts college in Pennsylvania; in 2015-2016, I serve as a Humanities Writ Large Fellow at Duke University. My scholarship is mostly on late nineteenth-century women writers and I’ve published in Legacy: A Journal of American Women Writers, American Literary Realism, modern fiction studies, and other journals; I also currently serve as editor of the Edith Wharton Review. My projects while at Duke synthesize my scholarly and pedagogical interests. Please watch this for comments on literature, mapping, digital humanities, travel, and life in Durham NC.