Lisa Marie Rhody

Information || Feminism || Poetry

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  • Curating a Network of Wood and Would

    Curating a Network of Wood and Would

    In my current research, I argue that Elizabeth Bishop’s poem “The Monument” represents a more democratic attitude toward aesthetic objects than what we see from her contemporaries Robert Lowell and John Berryman.  Eschewing the “tutelary” relationship between poet as teacher and reader as student, Bishop offsets her own position of power as the artist-creator by including the voice of a resistant, reluctant onlooker whose interrogations about what it is they are supposed to be looking at position the reader as the monument’s curator, one who must select between descriptions, views, depth, and purposes for the monument.  The monument’s physical presence is brought into being collaboratively between the two speakers who create it’s shape and it’s potential and the reader who must parse and prioritize the verbal network of the poem.  Furthermore, Bishop, who started writing “The Monument” in her Key West notebooks (see Barbara Page) after just having read Wallace Stevens’ Owl’s Clover, enters into public discourse about the relevance of public monuments (and by extension art) in a social, political, and economic climate in which people are suffering, nations are warring, and the realities of daily life seem to negate the place and purpose of art.  Sounds familiar, no?

    Bishop responds by arguing that monuments (and painting, and poetry, and sculpture) are significant because they are sites of “commemoration”–an interesting word choice because the word “commemorate” requires communal activity.  Unlike her friend Robert Lowell who uses the bronze relief by August Saint-Gaudens in Boston Commons to establish historical connection and significance for himself as an artist, Bishop imagines the monument as evolving to purpose rather deriving from it.

    Visualizing the poem as a network demonstrates how the speakers’ relationship to one another builds out of their discursive description of the monument.  In the networks below, each speaker is related to the monument through questions and description (recorded as the statements they make in the poem).  I have characterized those statements as grounding the monument in physical space, with tangible attributes, insisting on its materiality [repesented by blue lines] or on the other hand imagining the monument’s potential or possibility through questions, equivalences (is it this or that statements) and statements that intimate a metaphysical presence for the monument [in orange].

    <<image lost>>

    The networks are formed by matching each speaker up with with each statement made in the poem.  As a result, this is a “bimodal” network: one which takes actors and text and studies the relationship between them.  The relationship, represented by a line is further characterized as advancing the monument’s status as “wood” (a physical object belonging to the material, and therefore “real” world) and the word’s homophone “would” (a representation of possibility, potential, and by association the “imaginary” life of the mind).

    The reader as curator, then, must choose among the wood and the would–between the multiply rendered descriptions of the monument’s physical presence and its imagined potential, which is a new beginning itself, the shape of which could be poetry, painting, statue or monument, depending on choices the reader makes.

    Monument as Descriptive Network
  • Small Projects & Limited Datasets

    Small Projects & Limited Datasets

    I’ve been thinking a lot lately about the significance of small projects in an increasingly large-scale DH environment.  We seem almost inherently to know the value of “big data:” scale changes the name of the game.  Still, what about the smaller universes of projects with minimal budgets, fewer collaborators, and limited scopes, which also have large ambitions about what can be done using the digital resources we have on hand?  Rather than detracting from the import of big data projects, I, like Natalie Houston, am wondering what small projects offer the field and whether those potential outcomes are relevant and useful both in and of themselves as well as beneficial to large-scale projects, such as in fine-tuning initial results.

    My project in its current iteration involves a limited dataset of about 4500 poems and challenges rudimentary assumptions about a particular genre of poetry called ekphrasis—poems regarding the visual arts.  It is the capstone project to a dissertation in which I use the methods of social network analysis to explore socially-inscribed relationships between visual and verbal media and in which the results of my analysis are rendered visually to demonstrate the versatility and flexibility available to female poets writing ekphrastic poetry. My MITH project concludes my dissertation by demonstrating that network analysis is one way of disrupting existing paradigms for understanding the social-signification of ekphrastic poetry, but there are more methods available through computational tools such as text modeling, word frequency analysis, and classification that might also be useful.

    To this end, I’ve begun by asking three modest questions about ekphrastic poetry using a machine learning application called MALLET:

    1.) Could a computer learn to differentiate between ekphrastic poems by male and female poets?  In “Ekphrasis and the Other,” W.J.T. Mitchell argues that were we to read ekphrastic poems by women as opposed to ekphrastic poetry by men, that we might find a very different relationship between the active, speaking poetic voice and the passive, silent work of art—a dynamic which informs our primary understanding of how ekphrastic poetry operates.  Were this true and were the difference to occur within recurring topics and language use, a computer might be trained to recognize patterns more likely to co-occur in poetry by men or by women.

    2.) Will topic modeling of ekphrastic texts pick out “stillness” as one of the most common topics in the genre?  Much of the definition of ekphrasis revolves around the language of stillness: poetic texts, it has been argued, contemplate the stillness and muteness of the image with which it is engaged.  Stillness, metaphorically linked to muteness, breathlessness, and death, provides one of the most powerful rationales for an understanding how words and images relate to one another within the ut pictura poesis tradition—usually seen as an hostile encounter between rival forms of representation.  The argument to this point has been made largely on critical interpretations enacted through close readings of a limited number of texts.  Would a computer designed to recognize co-occurrences of words and assign those words to a “topic” based on the probability they would occur together also reveal a similar affiliation between stillness and death, muteness, even femininity?

    3.) Would a computer be able to ascertain stylistic and semantic differences between ekphrastic and non-ekphrastic texts and reliably classify them according to whether or not the subject of the poem is an aesthetic object or not?  We tend to believe that there are no real differences between how we describe the natural world as opposed to how we describe visual representations of the natural world.  We base this assumption on human, interpretive, close readings of  poetic texts; however, there is the potential that a computer might recognize subtle differences as statistically significant when considering hundreds of poems at a time.  If a classification program such as Mallet could reliably categorize texts according to ekphrastic and non-ekphrastic, it is possible that we have missed something along the way.

    In general, these are small questions constructed in such a way that there is a reasonable likelihood that we may get useful results.  (I purposefully choose the word results instead of answers, because none of these would be answers.  Instead the result of each study is designed to turn critics back to the texts with new questions.)  And yet, how do we distinguish between useful results and something else?  How do we know if it worked?  Lots of money is spent trying to answer this question about big data, but what about these small and mid-sized data sets?  Is there a threshold for how much data we need to be accurate and trustworthy?  Can we actually develop standards for how much data we need to ask particular kinds of humanities questions to make relevant discoveries?  In part, my project also addresses these questions, because otherwise, I can’t make convincing arguments about the humanities questions I’m asking.

    Small projects (even mid-sized projects with mid-sized datasets) offer the promise of richly encoded data that can be tested, reorganized, and applied flexibly to a variety of contexts without potentially becoming the entirety of a project director’s career.  The space between close, highly-supervised readings and distant, unsupervised analysis remains wide open as a field of study, and yet its potential value as a manageable, not wholly consuming, and reproducible option make it worth seriously considering.  What exactly can be accomplished by small and mid-scale projects is largely unknown, but it may well be that small and mid-sized projects are where many scholars will find the most satisfying and useful results.

  • Preparing texts for network visualization

    Preparing texts for network visualization

    When I presented at MSA 13 earlier this month, I was unsatisfied with my methods for creating network visualizations of texts.  I knew that preprocessing automatically would not work yet, since I have yet to identify precisely how I want to designate nodes across larger bodies of poems.  What I’ve been looking for is a way to mark texts up descriptively, using some form of markup language (XML, TEI), that would be uniform enough to render data that could be meaningfully displayed, and then to find a visualization software package with an algorithm that would “work” the way I wanted it to.  The problem, of course, is that when you’re a rogue DH scholar out in the world borrowing tools and using whatever tends to fall your way, then you’re not going to be sure about how each tool works (unless you have a CS or social science degree that includes learning about network algorithms, which I do not have), and this is going to detract from the validity of how and what you say about your object of study.  On the flip side, tools and text analysis software are becoming more widely available, and so doing what I’ve done, which is to say Googled “discourse network tool” and finding Philip Leifield’s “Discourse Network Analyzer” is actually possible.  What is remarkable about how DNA, a GUI text processing software, works is that it is designed as an interpretive tool to mark texts up in XML so that they can be displayed using free network visualizing software such as Visone, Ucinet, or Netdraw.  The designed purpose of Leifield’s DNA software is to collect articles on a topic area and to use those articles to create network visualizations of agreement and disagreement between individuals and groups.  For example, the sample dataset used for a tutorial on the software comes from someone at the University of Maryland named Dana R. Fischer, (I have no idea who she is… but I’m definitely going to look her up!) who marked up articles, testimony, and other texts about climate change.  Essentially, she could input each text into the DNA software and create a basic XML document with very minimal encoding (document type, author, dates, title) and then use DMA to select portions of text that create a “statement” about climate change.  By tagging the speaker, the organization the speaker is affiliated with, and the content type –a restricted list of terms created by the user to describe the topic being discussed—as well as whether or not the speaker agreed or disagreed with the topic) she could create networks of statements made about climate change that also included the individuals involved in the climate change debate and their organizations.  Such a visualization helps us to understand how much any one group (say, the Senate and the EPA) agree with one another, to identify the issues on which they agree and disagree, and to also understand affiliations (which speakers are affiliated with which climate change debates).

    This isn’t *exactly* what I had in mind, but it’s really darn close.  The power of this particular piece of software is that I can be in charge of what constitutes an article (a poem), what constitutes a speaker (the poetic speaker, the author, the third person omniscient… all of them), and the “content” to be described.  Granted the “organization” classification is less helpful to me, but in the instance of “The Venus Hottentot (1825)” I could differentiate between speakers from the first section of the poem from the second using this feature.  Using the software this way does not begin to utilize it’s real power, which is to read topics and speakers over large corpuses of texts in similar ways.  For now, I’m looking at one poem; however, I could see in the future were I to take this poem and situate it in a larger public discourse about black female subjectivity, I could.  I could import, for example, Sander Gilman’s article “Black Bodies, White Bodies: Toward an Iconography of Female Sexuality in Late Nineteenth-Century Art, Medicine, and Literature,” which we know Elizabeth Alexander read before writing the poem.  We could also bring in articles by Sadiah Quershi on “Displaying Sara Baartman” or Terri Francis’s “I and I: Elizabeth Alexander’s Collective First-Person Voice, the Witness and the Lure of Amnesia,” or chapters from Deborah Willis’s Hottentot Venus 2010 and demonstrate how Alexander’s poem participates in a larger act of social recovery.

    There are, as with any tool, limitations, though.  So far, the only way to create the visualizations is using the speakers, organizations, and categories with directional lines indicating agreement or disagreement.  I have not found a way of creating networks of “statements.”  In other words, I have not found a way to pull a category and then visualize the network of statements about that category and how they relate to each speaker; however, I have only begun the process of creating visualizations.  Another complication is that I have only found ways to make a statement associated with one category.  I’m fairly certain I can find a work around for that, but for the moment, that’s not worked out; however, I will say that having to choose between regular category designations (ones of my own creation) did make me very attuned to my assumptions about the text.  That process helped me to realize how my visualizations of these networks will always be limited and remind me that I need to make those limitations transparent when I write about what the visualization actually visualizes.

    In the meantime, even though I am not teaching right now, I’m really excited about what this kind of software could mean for my students.  In the English 101 courses at the University of Maryland, students write three linked assignment papers on a self-selected research topics.  These are position papers, where the student must make purposeful arguments for what he or she believes in and respond to the discourse of the field in which their selected debate is ongoing.  We generally assign an annotated bibliography as the first part of that linked assignment as a way of getting students to read the work and to then explain who agrees with each other on particular points and who disagrees.  The hard part of this assignment is that each entry is generally 2 paragraphs long and includes only 8-10 sources, and getting the students to actually compare arguments, identifying points of agreement and disagreement is difficult.  However, if the assignment were to use the Discourse Network Analyzer to import each article and then go through each article tagging “statements,” “speakers,” “organizations,” and “categories” (for example, are the speakers arguing that a particular action should be taken or that one event causes another…) as well as “agreement” or “disagreement” with that statement, they might begin to see how their readings create a network of ideas and by understanding who agrees and what they agree upon, the student might be better able to situate him or herself within the discourse of that issue.  It’s an intriguing idea to me, and at some point when I’m teaching again, I think I’m going to make use of this technology.