I got a couple requests to put my pictures from last night up on the blog, and I figured I’d share the non-collaged originals:
(Those of us trekking to the metro from the Hawk & Dove were at least pleased to pass by the scenic Capitol on our way!)
What an awesome hands-on visit to the Folger–thanks to all who played a role in organizing it!
Over the past week, it came to my attention that I have not been doing a very good job of not panicking in this class. While I did suspect that Matt was not likely to assign a failing grade to the majority of the class for not having Paper Machines working by the end of the meeting (the stated assignment), I spent most of that session feeling miserably confused. I probably looked quite grumpy. (I apologize to anyone at whom I may have inadvertently frowned.) While conversing with classmates in the days that followed about this thing that felt like a disaster — I had after all (for the first time ever) failed to complete a pass/fail assignment — I started slowly realizing that I may not be as hopelessly behind the curve as I felt. From the start, I was uncertain what to make of the assertion that failure is a good part of DH work; I thought “That’s all very well if you’re trying to make something just because you want it, but what about when my grade/job depends on success?” I realize that I have not been a very active participant in class so far, but I think I figured out that my real problem may not be that I am technologically inept (though I am), but that I habitually operate under assumptions about good academic work that are entirely incompatible the way DH defines good work. It will not be easy to murder the little perfectionist angel/devil on my shoulder, but it will probably do me some good if I manage it.
So, moving on, I went in search of the reason I ever even considered signing up for this class. Last year, I attended a Digital Dialogues lecture by Mike Witmore (the director of the Folger Shakespeare Library) called “Shakespeare from the Waist Down.” I had seen flyers for other dialogues, but I didn’t think they applied to me (due to the technological ineptitude thing), but I was curious enough to go to this one because it mentioned Shakespeare in the title. I was astonished by the idea that something as simple as pronoun patterns could enable a computer to tell the difference between comedy, history, and tragedy, and that it could identify the late romances (a modern editorial categorization). The link to the podcast of that talk is here, if anyone wants to watch it: http://mith.umd.edu/digitaldialogues/mp3/dd_2012_02_21.mov.
I am also really looking forward to the Folger visit tonight. The Shakespeare Quartos Archive is the first thing we’ve studied that I’ve actually seen before! I don’t know exactly what I may do, but I hope to pursue it further. Collation and archiving seem the sorts of things I am most likely to use in the future, so I’m happy to be on somewhat more familiar territory! So, stay tuned; I promise I’ll have something more interesting to say soon.
(And maybe, someday, I’ll even figure out how to take screenshots!)
I decided to use the first Public Writing Audit as a chance to do some Storifying. I’ve seen people use Storify, but I don’t think I ever quite understood how it actually worked. Going through all my tweets from this semester, which required a fair bit of searching through twitter with Storify’s functions, made me realize that this can be a tedious and time-consuming process. But I also think it holds great potential.
My Storify is organized around DH Events, What I’m Reading, PDA 2013, #TransformDH, ENGL 668K exercises and tweets, and Miscellaneous. It’s several pages long, so be prepared. It’s here:
http://storify.com/MelissaRogers17/668k-public-writing#publicize
Going through these tweets, I realized a few things.
1) I could create a Storify of all my tweets related to Star Trek and chronicle the exact dates and times I was watching certain episodes!! This would be fun but it would also reveal (to a number of eminent academic twitterists, including my adviser) just how much time I spend watching Star Trek….a lot. But, it would also be a fun way to write that mini-essay on Star Trek that’s just been itching to come out–I could annotate all my Star Trek tweets, which are usually quotes or “#wisdom” from my favorite characters, delightfully out of context. But you’re not here to read about Star Trek, so moving on….
2) A chronology of tweets reveals something different than a Storify of tweets organized around certain topics. I thought that first I would just put all the DH-related tweets in chronologically, but my immediate inclination while doing this was to group tweets together. Topics started to emerge. “#TransformDH” and “What I’m Reading” are the biggest categories. “Miscellaneous” tends to contain my (and others’) twitter snark. And there are usually clumps of tweets showing up right before and during our class time for ENGL 668K. BUT, these topics can also be plugged into a timeline. Rearranging them and putting them chronologically in order WITHIN the topics felt like putting together a narrative puzzle–”Oh right, that was the night I stayed up till 3am…twittering. Oh right, that Saturday six amazing conferences were going on at the same time…while I was home writing.” It made me think about my own story (and chronology) differently, which is exciting for a n4velg4zer errrrrrr ahem, autobiographer.
3) I thought my “public writing” on Twitter would be ME, writing, publicly. However, I’m a big retweeter. In fact, as I once observed on Twitter, retweeting is my jam. It often frustrates me to tweet from my computer as opposed to my phone, because my phone app allows me to easily quote other people’s tweets when retweeting, while I haven’t quite figured out how to do this on the computer. [Suggestions for useful PC twitter apps are welcome.] Often I retweet folks sharing snippets of events that I couldn’t attend, or pithy twitter poetics that are probably very decontextualized. So, I had to make a Storify decision as to whether I wanted this story to be just about/by me–impossible in the public writing context of Twitter.
4) I’m twitterpaited. The description on my Twitter profile, which I created more than a year and a half ago, suggests that I try not to be. And I really did try hard not to like Twitter. But then I found myself falling into Twitter holes all over the place, discovering all kinds of things. In fact, it has changed the way I do research. For example, I have a list entitled “Zine Love” that enables me to see all my zine-lovers’ tweets about things, zine-related and not. I also have an “Academia” list that enables me to see all tweets from the theory badasses I follow. But the problem with this is that I can’t be on Twitter all the time–it’s impossible. So as twitterpaited as I am, I will never be able to see ALL THE TWEETS. I have to be resigned to dipping into the stream when I have the time. [Which doesn't mean I resist the urge to endlessly scroll until I've caught up on all the action that happened since the last time I took a dip.]
I wanted to add up the total number of characters I tweeted, or figure out how many words it actually was since the Twitter gauntlet was thrown down early on in the course (What is a “significant” number of tweets? Is it the quality of the tweets or the quantity? What subject matter counts as public writing for this course?), but the Storify exhausted me. It will have to do on its own.
In terms of my blog posts for this class and for my own collaborative project, SqueakyWheelCollective.wordpress.com, the word count totals at least 4,573 (not including our exercises or words written after this sentence).
Dang. Now if I could only write that many words for that draft due in two weeks….
Stay tuned for the text of my Personal Digital Archiving Talk, “Public Displays of Affection: Digital Zine Archives and the Labor of Love.” [heh, c wut i did there? PDA, snicker snicker.]
Hi Everyone!
Just wanted to extend a dinner invite to anyone who’s interested – a few of us are eating at Nando’s Peri-Peri in Chinatown before going to the Folger Library tomorrow evening (since we’ll have to switch Metro lines anyway). If you’d like to join us, we’re planning on getting there at 5:45pm or so. We can eat, then take the Red line to Union Station. If you’d like to coordinate closer, feel free to email me, and I can give you my cell #. See you all tomorrow night!
- Charity
Here is a suggested fix for Paper Machines that has now worked in two cases, which gives me some confidence that it’s generally useful. Thanks to Courtney Wells, who volunteered her MacBook to test this fix. These instructions apply to Zotero running in Firefox (not standalone Zotero), and to Mac OS X systems. Here’s the summary:
Here are the details:
Please let me know if I’ve explained anything incompletely, or if this fix doesn’t work for you.
I was reading this recent article in The New Yorker digital edition and was struck by how apropos it is to our class. In his article “Structure,” non-fiction author John McPhee talks about how important structure is to writing a story, but most interesting to us digital humanists, talks about how a software program called Kedit (kay edit) profoundly changed the way he structures and lays out his stories. Even when newer software became available, he stuck to his anachronistic version because it was what he knew.
“Structure” strikes me as incredibly interesting for DHers because McPhee was writing when personal computers became available, and how he documents the way it impacted his work. I found it to be one of the first instances of the digital changing the humanities. Of course, nowadays, almost every author writes using some kind of word-processor, but McPhee had to switch from his typewriter to Kedit. It’s very enlightening to those of use who have always almost had the use of a personal computer.
This might just be me taking any excuse I can find to talk about an author I like, but the writings of Stanislaw Lem do seem relevant to DH work. Specifically, “How The World Was Saved” applies well to the principles of something like text analysis or topic modeling.
In the story, Trurl (a robotic engineer/constructor) builds a machine that could create anything starting with the letter N. Already this enacts a constraint, from which Trurl can create and build. Trurl must learn to work within this limit (and it is quickly established that there are no shortcuts to get outside of the machine’s programming). The difference, of course, is that what this machine creates also exists in the real world. In other word, by limiting its contents (or data) to a finite number, the programming of the machine enacts a “deformance” on the universe, as if it were a text.
We can also relate Trurl’s machine to the theory vs methodology debate. Trurl doesn’t approach the machine with a specific question he needs answering or an end result he would like to achieve. Instead, Trurl experiments, feeding the machine words and testing the limits of its programming. To relate this to Fish, Trurl does not begin with an interpretive hypothesis that needs answering, as is typical in the humanities. Likewise, he doesn’t apply this hypothesis to the machine’s results and look for a formal pattern. In DH, the direction is reversed (witnessing a formal pattern and then formulating an interpretive hypothesis). Trurl takes the DH approach.
Unfortunately for Trurl, the word he uses to reach this point, “Nothing,” nearly destroys the universe. But what is interesting is how the machine responds to Trurl’s command:
Had I made Nothing outright, in one fell swoop, everything would have ceased to exist, and that includes Trurl, the sky, the Universe, and you – and even myself. In which case who could say and to whom could it be said that the order was carried out and I am an efficient and capable machine?
Trurl’s experiment with “Nothing,” which unfortunately eliminates all of those “worches” and “zits,” can be considered the outlier in the machine’s results. It represents a moment in which, in order to follow both its programming and its command, the machine must alter its methodology somewhat (not to mention the universe itself). This might be the type of result we look for when running topic models or text analyses: the ones that confront what the tools struggles or fails to do or even strains itself doing.
Of course, Trurl’s machine is just science-fiction and doesn’t technically exist, but I wouldn’t mind experimenting with it myself.
(Side Note: Another of Lem’s stories, “Trurl’s Electronic Bard,” could have easily been the subject of another blog post, applying particularly well to our discussion in the first few weeks. But don’t worry, I’ll spare you the long-winded essay . . . for now.)
I came across this really interesting data today that speaks directly to everything we’ve been discussing in class: Twitter, topic modeling, word clouds, data collection. This site categorizes tweets in New York City based on the language that is being tweeted in. Here, we are given our topics (language), but it also gives us an idea of what the ethnic makeup and diversity of neighborhoods is within the five boroughs. Imagine it as a word cloud of what makes up the city!
Having lived in NYC for 10 years prior to moving to DC last year, I find this extremely enlightening, especially given that I was always told Queens (where I lived) was the most diverse county in the entire United States. Judging from this, Manhattan is way more diverse, at least in terms of languages spoken. I’m also shocked that Chinese isn’t one of the languages aggregated by this site, as there is a very large Chinese population in Flushing, Queens. Additionally, I lived in Astoria, Queens, which has a large Greek community. Prior to seeing this data, had I done an exercise similar to the farmer’s market exercise we did last night, I would have included topics/languages not seen here.
Make sure to zoom in and out from the streets and also use the roads-to-black scroll bar at the bottom for optimum choices. There is also a view of London.
We don’t really get to declare independence from Travis Brown’s technical support until we have Paper Machines actually running on our laptops. I’ve got everything working except topic modeling, and I think a lot of other people are in the same boat (although I know that Katie did manage to get it working last night). I’ve identified a Paper Machines bug report that may be the same problem we’re having (https://github.com/chrisjr/papermachines/issues/12), but I need more information to be sure. In the interest of getting topic modeling using Paper Machines working for myself (because I actually intend to use it), I’m volunteering to be the point person for debugging this issue. I think (to borrow from Mark Sample) this definitely counts as service, not scholarship.
Please comment on this post with your operating system, Java, and Python version information, plus whether or not Paper Machines is working for you. For operating system information on a Mac, click on the Apple in the top menu bar, and select “About This Mac”. For version information about Java and Python, you’ll need to open the Terminal application and then type “java -version” and “python -V” on the command line. I’m hoping that a Windows person in the class can supplement this with information on how to do the corresponding operations in that environment: since the last Windows OS that I was familiar with was XP Pro, I don’t think that anything I’ll have to say on the subject will be useful.
As an example, the OS on my laptop is Mac OS X Version 10.6.8, the Java version is 1.6.0_39, and the Python version is 2.6.1. I’ve got Paper Machine working except for topic modeling. I’d appreciate hearing from as many of you as possible, including those who have it working (because I need all the data points to see what the non-working installations have in common). Thanks.
Here’s a Word Cloud I made with Paper Machines from the Patristics sub-folder in our group.
