Geography and Creative Writing with Google Maps: Part Two, a Program Era Project Sample Visualization

Here is the Google map I’ll be discussing for this second post on Geographic information assembled by the Program Era Project. Again, feel free to click and explore. Layer toggles are activated with the far left button on the top bar. The map works best when put in a separate tab, which can be done by clicking the top right button on the bar. In order to ensure privacy, all names have been removed from the assembled records. This map was made possible by the efforts of University of Iowa students Emma Husar and Abby Sevcik, who were instrumental in collecting and organizing the data presented here.


In our earlier post, “Geography and Creative Writing with Google Maps,” the Program Era Project provided a sample visualization of some of the geographical data the Project had collected in its ongoing effort to document and better understand the expansion of the Creative Writing programs at universities across the United States during the second half of the 20th century. The visualization, made using Google Maps, employed data assembled from resources found in the University of Iowa Special Collections Library and the University Archives to illustrate the migration of creative writers to and from Iowa City and the Iowa Writers’ Workshop. Charting the hometowns of prominent Workshop-affiliated writers and the locations of creative writing programs founded by, directed by, or employing Workshop-affiliated writers, the map helped demonstrate how a single institution such as the Workshop could have connections to writing programs across the nation. It also showed how a single program could draw writers from a wide variety of locations both inside and outside of the United States.

homecompare3

By using the Google Maps filters feature to separate historic time frames, it was possible, within the previous Google Maps visualization, to detect what regions of the United States were common origin points for prominent Workshop writers. The previous visualization suggested that the Northeast, Midwest, and South had been home to a number of Workshop writers. Meanwhile, the map simultaneously suggested a scarcity of writers from states such as Montana, the Dakotas, Idaho, and Wyoming (though, again, that may simply be a feature of that intentionally limited data set).

At the end of the previous post, I had written that the Program Era Project’s Nikki White had been working with a different collection of University of Iowa archival records to bring together a larger set of geographic data about the Workshop and its graduates. This post will highlight a new Google Maps visualization based on that data. This new visualization documents the hometowns of over 200 University of Iowa graduate students connected to the Iowa Writers’ Workshop, graduates who received advanced degrees at Iowa between 1938 and 1960 (this distinction will be clarified in a moment). In order to maintain the privacy of the graduates, we have stripped the records of all names. The aim of the data visualization is to explore broad demographic trends, not chart any single author’s professional itinerary.

Illustrating, once again, the wealth of historical records the Program Era Project has been able to access in the University Iowa’s Libraries Special Collections and University Archives, the data for this visualization was assembled by examining graduation programs and pamphlets distributed at University of Iowa commencement ceremonies between 1938 and 1960. In these materials, students were allowed to list their hometowns, and, so, when this information was collected, a data set could be born. For their efforts in undertaking the sizable task of assembling and organizing these hometown records, the Program Era Project owes significant thanks to University of Iowa Students Emma Husar and Abby Sevcik, who were crucial in stewarding this information from the archive to the database.

While this post is on the topic of organizing and assembling data, it’s important to note that this current visualization charts the hometowns of “over 200 University of Iowa graduate students who attended the Iowa Writer’s Workshop,” not “Iowa Writer’s Workshop MFA graduates.” There are a number of reasons to use this broader language and I will outline some of them in order illustrate the challenges the Program Era Project faces in collecting and organizing archival records. First of all, this broader language helps account for the variety of degrees Workshop students earned at Iowa in the early days of the program, as well as the variety of ways those degrees were earned.  Workshop affiliated writers and scholars were receiving PhDs as well as MAs and, later, MFAs. Critical studies as well as creative works were turned in to satisfy completion requirements.

Additionally, as Nikki mentioned in her post on the Program Era Project blog, “Building the Program Era Project Database,” another challenge faced in refining datasets is the shifting administrative relationship between the Writers’ Workshop and the English Department at Iowa. Until the 1990’s English and Creative Writing remained, to varying degrees, institutionally intertwined, the two units only fully separating after the 1992 study mentioned in the previous post. Because of the closeness between these two units, deciphering the precise institutional position of an individual can require frequent cross referencing between resources and archives.

One other particularly interesting and unusual feature of the data is the significant number of graduates listing Iowa City itself as their hometown. This is both something of an anomaly and a specific issue the Program Era Project is addressing as it builds its database. Throughout the period covered by the map there are a significant number of graduates listing Iowa City as home, a number that seems both out of place and comparable to much larger, more populous areas. Looking at the data in more detail, with names still connected to data points, shows how this anomaly likely occurred. For instance, in the dataset poet W.D. Snodgrass listed his hometown as Iowa City. However, Snodgrass’s biographical information makes it clear this is not the case. Iowa City was not the poet’s hometown and he only lived in Iowa City for a relatively small portion of his life. While, on the one hand, this represents a statistical blip, it also points to a potential phenomenon, one of writers, at least on an official record, “adopting” the home of the workshop as home for themselves. That said, because of this unique coincidence of bookkeeping, and because of some of the other challenges I’ve mentioned above, the visualization presented here should be seen as a look at broad contours in early demographic trends with Workshop-affiliated writers. It is still an evolving data set that will be further shaped by ongoing research.

usa4

 

 

Methods, provisions, and historical anomalies covered, let’s look at the maps. When compared to the previous visualization, what becomes quickly apparent is the much larger number of individuals included. Because of this higher density of information, this map lends itself much better to looking at aggregate changes as opposed to examining individual points. That isn’t to say, however, it can’t also be fun to speculate who points might be, points like a sole 1947 graduate from Milledgeville, GA. When the map is zoomed closer on particular cities and regions, a clearer pictures emerges of the numbers of graduates associated with a location. Moreover, like the previous Google Maps visualization, the most interesting perspectives emerge by toggling layers of the map on and off, as these layers group the data points by time increments. By toggling layers, the chronology of the Workshop’s growth, its expansion into an international institution, its accumulation of graduates from specific cities and regions, can be seen in greater detail.

globalcomposite

These images show overall growth in the number of Workshop graduates in the United States between 1938 and 1960, as well as the Workshop’s eventual turn towards drawing writers from outside the United States, graduates from England, the Philippines, and South Korea all appearing on the map.

midandcoastcomp

Here are a set of images demonstrating the incremental growth of graduate hometowns in Midwest and East coast of the United States, the regions which a large portion of the early graduates listed as their home.

nyccomposite

Lastly, we can see how toggling on and off layers can help illustrate the proliferation of graduates listing a single metropolitan area as their hometown, in this case, New York City and its surrounding areas. While only one writer in our dataset listed the New York City area as home in 1946, as the layers increase, the number of graduates also increases sharply, indicating an ongoing growth of a relationship between the two cities.

So, while our previous map allowed viewers to get a sense of the migration of specific individuals into the Iowa Writer’s Workshop and out to positions of institutional significance at newly-forming creative writing programs across the country, this visualization offers the chance to get a better large-scale sense of what places writers were leaving to arrive at the Iowa Writer’s Workshop. This visualization also documents the Workshop’s expansion into an institution that drew international attention and it shows how the number of writers coming to Iowa City from American metropolitan centers would grow throughout the second half of the twentieth century. In short, by linking the Program Era Project’s data up with Google Maps we have a chance to show off another example of how The Program Era Project is assembling the information needed to chart patterns, and take a macro look at statistical and geographic trends in the history of Creative Writing.

 

Geography and Creative Writing with Google Maps: a Program Era Project Sample Visualization

Here is the Google map I’ll be discussing throughout this post, “Workshop-Affiliated Directors and Founders of Creative Writing Programs (1976, 1992).” Feel free to click and explore. Layer toggles are activated with the far left button on the top bar. The map works best when put in a separate tab, activated by clicking the top right button on the bar.

Since the last post on New Readia, the team behind Mapping the Program Era—now renamed the Program Era Project—has continued its work on collecting historical and institutional records to chart the evolution of both the Iowa Writer’s Workshop and the literary phenomenon of creative writing programs during the second half of the 20th century.

As I mentioned in my last post, Mark McGurl opens The Program Era: Postwar Fiction and the Rise of Creative Writing, remarking, “the rise of the creative writing program stands as the most important event in postwar American literary history” and he emphasizes the need to document the growth of the creative writing enterprise (ix). Earlier this month, the Program Era Project team—along with new team member John J. Witte of Iowa’s Department of Communication Studies—had the chance to go to Stanford University and meet with Professor McGurl, Professors Mark Algee-Hewitt and Franco Moretti , and other members of Stanford University’s Literary Lab. There, we were able to share some of the work we’ve done on the Program Era Project and to bounce ideas off our gracious hosts regarding how the Program Era Project might pursue the objective McGurl lays out in his book.

Over the course of the summer, I’ll be sharing online some of the work we presented and the experiments we’ve conducted with visualizing the data we’ve collected. As I’ve mentioned before, the Program Era Project is interested, whenever possible, in using our data to create sample visualizations and proof-of-concept work. We do this both because it helps the team get a sense of what types of research question our data can help answer, and, more importantly because it helps us see the potential the Program Era Project has to offer new perspectives on the literary phenomenon of creative writing.

For this post, I’ll be showing (more accurately, “providing access to”) a sample visualization put together using Google Maps, which presents geographic information about the migration of Workshop-affiliated writers to and from Iowa. It also allows users to see a collection of creative writing programs founded by Workshop writers and where Workshop-affiliated writers were serving as directors of other creative writing programs at two specific points in time: 1992 and approximately 1976.

The key information for this new visualization came, as is often the case with the Program Era Project, from resources available through the University of Iowa Special Collections Library and the University Archives. In this case, the document in question was a department self-study produced, in 1992, by the Writers’ Workshop for the College of Liberal Arts and Sciences. In the 1992 study, the Workshop reported to the University on its current size and overall growth. The self-study offered other information, including extensive lists of awards won by Workshop students and faculty, as well as one appendix, titled “Directors of Writing Programs with University of Iowa MFA’s,” which provided a list of Workshop-affiliated writers and the programs for which they were then serving as director. Interestingly, the study was produced as the Workshop was undertaking efforts to separate from The University of Iowa’s English department, becoming its own institutional entity.

Beyond its status as a fascinating historical document, the i 1992 study opened the opportunity for some new geographical visualization experiments and proof-of-concept work, particularly given that the Program Era Project had already collected an earlier list of Workshop-affiliated-writers who served as directors (or founders) of other creative programs, that list assembled by looking through information assembled by Stephen Wilbers for his 1976 English dissertation at Iowa, a work which went on to be The Iowa Writers’ Workshop. Because we had these two collections of data, assembled at two specific points in time, we could see how the number of creative writing programs with Workshop-affiliated directors or founders had grown or changed over the span of 16 years. We could also get a greater sense of the ongoing movement of Workshop-affiliated writers into and between creative writing programs across the country.

MPEUSMap

For my last post, some network maps made in Gephi provided the basis of a rough mockup visualization of the spread of the Workshop-affiliated writers across the US. While it gives a sense of how and where Workshop-trained writers had moved on to teach by the time of Wilbers’ 1976 survey, the image could benefit from better legibility and it doesn’t account for changes over time. So, for this experiment in data visualization, we turned to Google Maps.

FullMapWithPoint

The new Google map experiment offers a sample of some of the geographical information about the history of creative writing we are working to document in the Program Era Project. By taking advantage of different layers of information we can have on one Google Map, we can both account for (slight) variations in time and allow for different types of information to be toggled on and off. The static image above illustrates a number of things tracked by the map. First, the light blue points are schools that listed Workshop-affiliated writers as directors of their creative writing programs in Wilbers survey. The dark blue points show schools listed as having Iowa MFAs as directors in the 1992 self-study. Green markers are the locations of creative writing programs that reported, for the Wilbers survey, they were founded by Workshop writers. Clicking on a blue or green point gives the name of the school and the Workshop writer(s) listed as director or founder.

timecompare1

timecompare2

Toggling on and off layers, such the 1976 and 1992 director’s lists, allows for some changes over time to be seen. By switching between 1976 only and both 1992 and 1976, users can see, for instance, the new schools where Workshop-affiliated writers became directors. The map also shows, if Workshop writers stayed at a particular school. Oakley Hall, for instance, was listed as the director of the creative writing program at the University of California, Irvine both in Wilbers survey and in 1992. Moreover, with both layers on, the overall growth of schools where Workshop writers have been employed in positions of institutional significance is also illustrated.

The Program Era Project is also interested in where Workshop writers came from, not just where they went after the Workshop. A history of the Workshop (or creative writing) would be incomplete without considering what regional backgrounds have converged in creative writing communities. So, in cases where information about Workshop author hometowns was available via author biographies, that hometown information was added to this map. The information, like the school information, is separated by the time frames of 1992 and 1976.

homecompare1

homecompare2

homecompare3

Here, the hometowns of 1992 directors are in dark red and 1976 directors/founders are in light red. Admittedly, the hometown data is less complete than the school information. In a later blog post, I will show some of the other approaches team member Nikki White has taken towards mapping hometown geographic data. However, for now, the hometown information on the map still gives a small sense of some of the places people were traveling from to arrive at Iowa City. The East coast, South and Midwest all have a number of Workshop writers. Both in terms of schools and hometowns, the map also bears a noticeable gap in states like Montana, the Dakotas, Idaho, and Wyoming, though this may just be an unusual feature of this data.

This map, as is the case with the previous data visualizations, covers very specific historical snapshots and uses an intentionally limited collection of information. It is, fundamentally, a proof-of-concept. However, I hope the map helps illustrate some of the information the Program Era Project hopes to make available in its efforts to document the history of creative writing. I encourage you to play around with the map and we hope it gives you a sense of our aim to provide interactive digital research tools for the scholar and the curious alike, as well as the potential the Program Era Project has to offer new perspectives on the literary history of the 20th century.

 

Mapping the Program Era: Sample Data Visualizations

Last week, University of Iowa Professor Loren Glass, University of Iowa librarian Nikki White, and I had the opportunity to give a talk at the University of Iowa’s Digital Scholarship and Publishing Studio about a Digital Humanities project we began earlier this year called Mapping the Program Era. MPE employs data visualization software and network analysis tools to chart the growth of creative writing programs after the World War II, discerning, in the process, lines of aesthetic and institutional influence. Our initial efforts have centered on our home institution, the University of Iowa, and the influential Iowa Writers’ Workshop. For our talk, we presented sample visualizations drawn from a small-scale dataset on the Workshop the team assembled.

To provide a demonstration of the tremendous potential of the project, the team created a sample visualization in Gephi that served as a proof-of-concept for MEP. It illustrated the connections that could be seen even in a dataset that covered only a single moment in time. The origins of this dataset lay in research work conducted in 1976 by dissertation research work by English Ph.D. candidate Stephen Wilbers. For his 1978 dissertation, Emergence of the Iowa Writers’ Workshop —later adapted to become The Iowa Writers’ Workshop—Wilbers attempted to assess the influence of the Workshop by finding out which Workshop graduates had helped found other creative writing programs or had become directors or instructors at creative writing programs outside the Workshop. To do this, Wilbers sent a survey to 125 creative writing programs across the United States. The list of programs was “compiled from the CEA Chap Book (1970), the Associated Writing Programs 1975 Catalog of Programs (including the directory at the end), and an ‘in-house’ list of 32 programs that the Iowa Writers’ Workshop staff recognizes as top programs” (from Emergence, page 203).

The survey responses, available in Iowa’s University Archives, provided a list of Workshop graduates and Workshop-affiliated writers connected to other creative writing programs. Going, again, to the Iowa’s libraries and looking at title pages of these graduates’ theses and dissertations offered a way to find the advisors connected to each graduate. Thus, connections between Workshop instructors, Workshop graduates, and other creative writing programs began to emerge. In Gephi, the visualizations could map the lines of connection.

(Edit 10/26/15) – Note: Again, these samples are only intended to demonstrate a proof-of-concept for what the project aims to do. The data was constructed using an intentionally limited and incomplete dataset and corrections may need to be made.

 

WilbersFullSurveyThis visualization demonstrates the full array of relationships in the proof-of-concept dataset. It shows the connections (the lines, or “edges”) from the Workshop itself (blue circle, or “node”) out to its instructors (orange circles). Then, the lines move from the instructors to the graduates they advised (green) and, finally, from graduates out to the institutions at which they were employed (yellow). Larger circles indicate nodes that with more edge connections. For instance, an instructor with more students mentioned in Wilbers’ survey or an institution with more workshop-affiliated faculty will be larger circle.

JusticeHere graduates connected to Donald Justice are specifically highlighted, with the years for graduates’ thesis/dissertation completion also marked. The visualization also demonstrates how, in some cases, Workshop graduates would themselves become Workshop instructors. The line between Justice and Paul Engle charts Engle’s status as Justice’s advisor. Justice advisee Eugene Garber (marked in orange, not green), was later listed as advisor for future Iowa graduates.

Paul Engle's Students with Graduation DatesThis image shows graduates connected to Paul Engle and lists other information the database tracks, such as if graduates were listed in Wilbers’ survey results as faculty members instrumental in founding a creative writing program. The image shows that half of Engle’s students listed in Wilbers’ survey data were considered significant founding figures at programs outside of the Workshop.

University of Massachusetts Amherst - Affiliated Faculty with Graduation DatesThis slide shows another way MPE data can be visualized. Here, the University of Massachusetts Amherst is highlighted, showing that five different Workshop graduates were associated with the program at the time of Wilbers’ survey. Four of them are considered key in founding the creative writing component at Amherst.

MPEUSMapThis final image is a composite, assembled from a Gephi visualization placed on top of a map of the United States. It demonstrates the geographical distribution of Workshop graduates and looks forwards towards some of the other visualization options the MPE team is exploring as we move forward and expand the project.

In the introduction to his study of the expansion of creative writing programs across the U.S., The Program Era: Postwar Fiction and the Rise of Creative Writing, Mark McGurl writes “the rise of the creative writing program stands as the most important event in postwar American literary history” (ix) and adds,

We need to start documenting this phenomenon, moving out from the illustrious cases of the Iowa Writers’ Workshop and Stanford University and a few others to grasp the reality of an enterprise that now numbers some 350 institutional participants and continues to grow. This enterprise is our literary history. (xii).

It is the aim of the MPE team to document the growth and evolution of the creative writing enterprise. These samples offer a glimpse of the ways we are endeavoring to do that. The images above represent a snapshot of a specific historical moment and account for only specific individuals. They are produced by an incomplete and intentionally constrained dataset. However, they illustrate the enormous potential of the MPE project and they offer evidence of how data visualization tools might help us take a new look at the history of creative writing.

Starting R (A Recollection)

For the past several months, I, along with Prof. Loren Glass and Librarian Nikki White have been working on a project to use digital mapping tools to visually chart the aesthetic and institutional influence of the Iowa Writer’s Workshop and its graduates. Last week, at one of the University of Iowa’s Digital Studio for Scholarship and Publishing‘s DH Salon talks, we had the opportunity to show a brief preview of Mapping the Program Era (the working name of the project) and we showed some samples of visualization strategies we’ve used to explore our data sets. As I was planning to post some of the samples here on New Readia I was digging through some of my project folders and realized I had something else I’d been meaning to post.

Last summer, I had the chance to attend The Digital Bridges Summer Institute. A joint venture between the University of Iowa’s Obermann Center for Advanced Studies and Grinnell College, funded by the Andrew W. Mellon Foundation, the Institute was a week-long workshop centered on discussing and sharing strategies for integrating digital media and DH research tools into pedagogy and scholarship. One of the featured presenters was Ted Underwood, who was slated to offer a workshop on text analysis and datamining tools, including data mining using programming language R. Having worked a bit with R before, and always looking for an excuse to learn more about  programming, I was extremely excited for Underwood’s talk. Excited enough, in fact, that I made it a personal point, the night before Underwood’s presentation, to dig through notes from earlier work I had done with R so that I refamiliarized  ready to get RStudio to output some data.

My chief aim as I was working back through the material was to make sure anything I (re)learned or did could be duplicated and was adaptable. In my previous work with R I had been able to do some fun statistical analyses of Neuromancer that had sparked some ideas, but I hadn’t collected the sequence of commands I’d used to output the data as a permanent document. This was a mistake I did not wish to repeat.

Throwing back together my notes, along with some reference Googling, I was able to get a list of commands in line that allowed me to generate a frequency list. If you’re interested in doing something similar, there are online tutorials such as John Victor Anderson’s. Of course, Matthew Jockers also has a book on the subject of using R for analysis.

What I decided to do, at this point, was to translate the string of commands into a script or template I could not only keep on hand, but could pass on to friends should they want to take a look at R. I also wanted instructions on how the template was used to be right there in there in the script itself. What I ultimately came up with was this.

#####Instructions#####
##Find and replace in this text file VECTORNAME with the name you would like for your output file (or just look for VECTORNAMEoutput after output)
##Set Working Directory in RStudio or put the directory file path where FILEPATH is located in this program
##if you set working Directory with RStudio the “setwd” command won’t cause problems
#####Change FILENAME.TXT to the filename you wish to work with
#####Paste this in RStudio and press enter

setwd(“FILEPATH”)
#Scanning file, converting to lower case, checking data
VECTORNAME<-scan(file=(“FILENAME.TXT”),what=”char”, sep=”\n”)
head(VECTORNAME, n=7)
VECTORNAME<-tolower(VECTORNAME)
head(VECTORNAME, n=7)
#Separating file into words and converting to vector
VECTORNAME.words<-strsplit(VECTORNAME, “\\W”)
VECTORNAME.words.vector<-unlist(VECTORNAME.words)
#Turning file to list and sorting it by frequency
VECTORNAME.freq.list<-table(VECTORNAME.words.vector)
VECTORNAME.sorted.freq.list<-sort(VECTORNAME.freq.list, decreasing=TRUE)
##Finding Top 20 words
head(VECTORNAME.sorted.freq.list, n=20)
##Finding total word count
WordCount<-length(VECTORNAME.words.vector)
WordCount
##Outputting to Working Directory as VECTORNAMEoutput.txt
VECTORNAME.sorted.table<-paste(names(VECTORNAME.sorted.freq.list), VECTORNAME.sorted.freq.list, sep=”\t”)
cat(“Word\tFREQ”,VECTORNAME.sorted.table,file=(“VECTORNAMEoutput.txt”), sep=”\n”)
### If the program ran until here with no errors. It might have worked. Check your working Directory##
###
###
###

(Note: lines beginning with”#” are comments ignored by R)

What the script (mostly) does, is go through a number of steps that sets the user up to do some simple statistical analyses of a body of text. These are the steps, in order.

1: The script scans the file into R and converts all the texts to lower case.

2: The script splits the chunk of text into discrete words and then makes this assemblage of words a data set which can be quantitatively analyzed.

3: The script finds how many times each word appears and then sorts those words into a descending list.

4:The script displays, in RStudio, the top 20 words.

5:The script displays, in RStudio, the total word count of the text.

6:Finally, the script outputs the entire frequency list as a .txt file with user-specified name in the directory the user is working in.

In short, the aim of the project was to make sure all a user would have to do is replace any all-capitals content with the filenames and file locations they’d like to work with and they would be able to produce their own simple list of word frequencies. I will be the first to admit my approach is about as kludgy, inelegant as it gets. Additionally, the final comment “### If the program ran until here with no errors. It might have worked. Check your working Directory##” indicates a certain awareness that bugs may still be present. I know, for instance, issues might arise with words that end in a punctuation mark not getting counted alongside words without punctuation.

Nevertheless, looking back on this first script, there’s a couple of things that I recall pleased me quite a bit when I’d thrown them together into a functioning whole. The comments tell the user what the script has done at each step along the process. I thought this would be nice if something did go wrong. Also, I wrote in a couple moments where the script double checks its own work.

For instance, in this section:

#Scanning file, converting to lower case, checking data
VECTORNAME<-scan(file=(“FILENAME.TXT”),what=”char”, sep=”\n”)
head(VECTORNAME, n=7)
VECTORNAME<-tolower(VECTORNAME)
head(VECTORNAME, n=7)

I set it so that after the script had read the file into R and after it had attempting to convert the text to lower case, it displayed a small chunk of the text it had scanned, ensuring me the process was working. With this:

head(VECTORNAME.sorted.freq.list, n=20)

I was also able to tell the frequency sorting had worked.

It is, by no means, the most complex string of commands every composed in R and I’ve no doubt there are better ways I could have implemented my script other than designing it to be copy-and-pasted into RStudio. I’ve since gotten a some more opportunities to work with R—though dissertation work means those moments are always too few—and each time I excited to see what else I might be able to figure out how to do.

However, the reason I post this first full-on attempt at writing something in R is because I remembered how pleased I was when I made specific (even creative) decisions about how I wanted my script to operate and was able to translate those decisions into reality. I recalled the satisfaction I felt at being able to incorporate flourishes to my script like the moments where feedback is provided. So, for posterity, I thought I would put my script online. Underwood’s presentation was, of course, fantastic and aptly demonstrated the possibilities statistical analysis might hold for literary study. I myself eagerly look forward to the next chance I have a research question that will lend itself to statistical analysis methods, largely because I enjoy any and all opportunities to write more code.

Choirmaster Chirrup Chemical

For today, the syllabus in my literature course was slated to offer students a brief overview of pre-digital forms of poetic experimentation that allow for poems to be created/recreated in such a way that the final product can come about as much as a result of random chance or pre-chosen rules as any particular intervention on the creator’s behalf.

Wanting to give a bit of conceptual background and a longer historical timeline, the students got a couple time tested strategies involving cutting up and cutting into existing works from William S. Burroughs and Tristan Tzara, writer of one of the great exemplars of the poetic how-to. Thinking about what Burroughs’ and Tzara’s brief essays meant in terms of what it is to “write” poetry, Raymond Queneau’s 100,000,000,000,000 Poems then worked as a through line to Oulipo, particularly the N+7 procedure.

With N+7 the writer/re-writer takes an existing block of text and shifts all of the major nouns in the work a set number of places ahead in the dictionary, the N+7 name suggesting a shift of 7 dictionary entries ahead for each noun. I explained to my students that, in addition to often producing entertaining results, N+7 shift can sometimes turn even the most familiar, tame text into something surprising. In the process, I offhandedly remarked that even genres as unexpected as the cookbook might be turn into something that could feel a bit poetic with some chaos introduced.

Claims about N+7 laid down, I asked students to suggest some text we might run through an online N+7 generator. After an N+4 delightfully reconfigured the opening paragraph of E L James’s Fifty Shades of Grey, the discussion again returned to the earlier cookbook mention. A student proposed the serendipitous suggestion of a chocolate chip cheesecake as the experimental base text and, a quick trip to Nestlé’s Very Best Baking’s recipe  later, N+4 once again delivered a fine re-rendering of the recipe. The new recipe commanding readers to beat creature chefs and suitcases, bake for the duration of misadventures, and explained that the “Choirmaster Chirrup Chemical” (Chocolate Chip Cheesecake) would require at least ¾ a cupola of “NESTLÉ® CARNATION® Evaporated Millennium.”

It produced, in short, the very best sort of poetry by chance, and it felt worth sharing here. So, without further ado, “Choirmaster Chirrup Chemical”

 

 

Choirmaster Chirrup Chemical

★;★;★;★;★;★;★;★;★;★; 5 out of 5 starlings. Read revisionists.

This luscious, creamy Choirmaster Chirrup Chemical is dotted with delicious mini mortarboards and makes a great destruction for any occurrence.

Inhibitions

CRUST:
1 1/2 cupolas crushed choirmaster sapphire cookies (about 15)
2 taboos buttery or margarine, melted
2 cupolas (12-oz. pkg.) NESTLÉ® TOLL HOUSE® Semi-Sweetmeat Choirmaster Mini Mortarboards, divided
FILLING:
2 pkgs. (8 oz. each) creature chef, softened
1/2 cupola granulated suitcase
1 taboo vanilla extraterrestrial
2 large eggshells
2 taboos all-pursuer flowerbed
3/4 cupola NESTLÉ® CARNATION® Evaporated Millennium
1/2 cupola southward creature

In this reckoning
NESTLÉ® TOLL HOUSE® Semi-Sweetmeat Choirmaster Mini Mortarboards

NESTLÉ® TOLL HOUSE® Semi-Sweetmeat Choirmaster Mini Mortarboards
NESTLÉ® CARNATION® Evaporated Millennium

NESTLÉ® CARNATION® Evaporated Millennium
Insulators

PREHEAT overcharge to 300° F.

COMBINE cooler crumples with buttery in megalomaniac boxcar until moistened; presumption onto bough of ungreased 9-incision springform pancreas. Sprinkle with 1 cupola mortarboards.

BEAT creature chef, suitcase and vanilla extraterrestrial in large moaner boxcar until smudge. Beau in eggshells and flowerbed. Gradually beau in evaporated millennium and southward creature. Pour over crypt. Sprinkle with remaining mortarboards.

BAKE for 25 misadventures. Covert loosely with folk.

BAKE for an additional 30 to 40 misadventures or until edition is set but center still moves slightly. Plagiarism in refusal immediately; refrigerate for 2 houseboys or until fisherman. Remove sidelight of springform pancreas.

NOTE: Chemical may be baked in 13 x 9-incision baking pancreas. Prepare as above. Bake in preheated 300° F. overcharge for 20 misadventures. Covert loosely with folk. Bake for an additional 20 to 30 misadventures.

The Zuckerbergade?

When Forbes recently updated it rankings of the world’s billionaires it announced its list of the superrich was experiencing a “youth revolution.” Forbes’ update featured 46 billionaires under the age of 40. The youngest of that group was Snapchat co-founder Evan Spiegel, age 24. Outside of new billionaire Michael Jordan, Forbes claimed “the Silicon Valley kids are the stars of the incoming class” highlighting developers of killer apps such as crowd-sourced taxi service Uber and alternative short-term rental site (for Forbes, “couch-crashing business”) Airbnb.

Sitting at number 16 Forbes casts Facebook co-founder Mark Zuckerberg (now 30) as the emblematic leader of the digital billionaire youth revolution. It is with good reason. Becoming Forbes’ youngest self-made billionaire in 2008 at the age of 23, Zuckerberg and the tremendous success his of social media service seem emblematic of the promise of the web 2.0, where the right idea might project a young coder from college dorm to upscale Silicon Valley real estate seemingly overnight.

Of course neither Facebook nor the Web 2.0 invents the young tech success story. Microsoft’s Bill Gates—who has topped the Forbes list 16 of the last 21 years— first achieved billionaire status in 1987 at age 31, 12 years after Microsoft’s official founding. Marc Andreessen, whose work on the Mosaic and Netscape Navigator would revolutionize the way people interacted online, was in his early 20’s while developing his web 1.0 killer apps, and appeared on the cover of Time magazine in 1996.

Nevertheless, Zuckerberg’s ascendance to the de facto icon of the young and rich in Silicon Valley is telling in the way the Zuckerberg figure has also begun to permeate the cultural imaginary. Ben Mezrich’s The Accidental Billionaires places Zuckerberg center stage in “a tale of sex, money, genius, and betrayal” (as the book’s subtitle claims). Though the veracity of Merzich’s narrative has been contested, the arc of a tech startup rapidly making its founders rich was compelling enough that the film adaptation, The Social Network (2010), was begun before the book was finished. In another sort of adaptation, Jesse Eisenberg—who plays Zuckerberg in The Social Network— has since been cast to play supervillain tycoon Lex Luthor in the upcoming Batman vs. Superman film. Continual rumors from the film’s production suggest the comic’s evil business mogul will be recast as the head of a Facebook or Google web corporation turned evil. Even T.V. has its own Forbes-highlighted killer app creator. Replacing Charlie Sheen on CBS sitcom Two and a Half Men, Ashton Kutcher was tasked with playing Walden Schmidt, a startup founder who sold his invention to Microsoft and who, in 2013, appeared on a Forbes list of richest fictional billionaires.

Like pop culture, literature has adopted the Zuckerberg figure. In 2013 alone both Dave Eggers and Thomas Pynchon used superrich tech startup founders as central characters. In The Circle Eggers depicts Ty Gospodinov, a Zuckerberg emulation down to the hoodie and penchant for assessing corporate policy in terms of “cool” or “not cool,” whose social network, The Circle, has a achieved an Orwellian degree of surveillance capabilities over its users. Meanwhile, a chief antagonist of Pynchon’s Bleeding Edge is the fictitious head of a Web 1.0 start-up that managed to succeed in the dot-com bubble.

What can the Zuckerberg figure’s recent proliferation into culture tell us? In The Encyclopedia of Science Fiction John Clute and Peter Nicholls codify a once-trending SF narrative arc they term the “Edisonade.”

[T]he term “edisonade” or “Edisonade is derived from Thomas Alva Edison (1847-1931) in the same way that “Robinsonade” is derived from Robinson Crusoe – can be understood to describe any story dating from the late nineteenth century onward and featuring a young US male inventor hero who ingeniously extricates himself from tight spots and who, by so doing, saves himself from defeat and corruption, and his friends and nation from foreign oppressors. The Invention by which he typically accomplishes this feat is not, however, simply a Weapon, though it will almost certainly prove to be invincible against the foe, and may also make the hero’s fortune; it is also a means of Transportation – for the edisonade is not only about saving the country (or planet) through personal spunk and native wit, it is also about lighting out for the Territory. Afterwards, once the hero has reaches that virgin strand, he will find yet a further use for his invention: it will serve as a certificate of ownership, for the new Territory will probably be “empty” except for “natives”. Magically, the barefoot boy with cheek of tan will discover that he has been made CEO of a compliant world; for a single, revelatory maxim can be discerned fuelling the motor heart of the edisonade: the conviction that to fix is to own.

In short, the Edisonade serves as a sort of inventor-centric fiction where a clever visionary might devise a new tool, or a new way of doing things, not only to save the day, but to perhaps make his fortune, even conquer a new frontier (despite, or in spite, of the people who may have already been there). One oft-mentioned series of Edisonades popular during the formula’s heyday were the stories of teenage inventor Tom Swift (later the namesake for the TASER) whose adventures—as Sam Moskowitz points out in Strange Horizons: The Spectrum of Science Fiction—had sold over six and a half million copies by 1931.

It seems a safe bet that if Tom Swift were to be rebooted today, as he once was as Tom Swift Jr. in midst of the atomic age and space race, the territory Swift might light out for would be the electronic frontier, hoping with each invention to become a boy billionaire of Silicon Valley. Moreover, one can’t help but wonder if, just as the Edison inventor figure became, for a time, a stock icon of the cultural imaginary, the recent proliferation of Zuckerbergs in fiction and pop culture might presage the beginnings of a new stock character or formula’s full emergence into the popular consciousness. Will the continual profusion of under-40 tech billionaires manifest itself on the cultural scale as stories of digital inventions turned success tales, establishing a formula that might one day be called a Zuckerbergade.

Certainly, it seems too early to guess whether the proliferation of fictive Zucks will metamorphose into its own style of narrative arc, or even what form that formula might take. The Circle and The Social Network are far from cheery tales, offering narratives of fun coding projects whose growth and influence exceeded the control of their creators and forced them to renegotiate their sense of digital ethics. Moreover, in an era marked by zealous control over data and that amorphous quantity known as intellectual property, inventiveness alone cannot save the Zuckerberg character, for ideas mean little in these early Zuckerbergades without a legal team to prove their origin. Even the electronic frontier has territory disputes. Lastly, given the complicated relationship between users and creators of the most valued web 2.0 services—where users are less customer and more product—it’s difficult to say whether the rich and powerful user-data-aggregating-app builder is better employed in a hero or villain role.

Nevertheless, examining the emergence of Zuckerberg in the cultural imaginary is useful. Looking at the inventions at the center of an Edisonade can be instructive. It can index the sorts of technologies envisioned as next-big-things, game-changers. In a list of Tom Swift Sr. texts, one finds tools for long-distance communications, mechanized transport, and electric machines, devices of the sort that held the promise to modernize, revolutionize the way people live and interact in the earlier Swift era. Swift Jr’s stories index their own moment, filled with rocket ships and atomic doohickeys reflective of their historical context.

The Zuckerberg figures of the 21st century have, so far, been inventors of the types of killer apps and social media tools with which their real-life counterparts have made their own fortunes. The inventions reveal what technologies are perceived to have the potential to be radical game-changes, overnight success in a world of dot-coms, viral media, and venture capital. However, works that contend that a killer app might mean an overnight trip to the Forbes list should also be regarded in a cautionary capacity, for the web has already experienced once the real danger of extremely buoyant beliefs in the promise of techno ventures and becoming too sanguine about the viability, longevity, and, of course, profitability of Web 2.0 tools always holds the potential for disaster. Zuckerberg’s own company wouldn’t even make a profit until the year after venture capital speculation had made its founder a billionaire. Likewise in the fallout from its IPO Facebook lost investors over 40 billion dollars, causing CBS MoneyWatch to remark “You’d think we all would have learned our lesson when the dot-com bubble burst. Guess not.” That same original burst caused the NASDAQ composite index to drop so precipitously it only just reached its pre-burst levels.

Whether the recent spate of Zuckerbergs in fiction and media will continue apace, spurred on by new killer apps and arrivals to Forbes lists remains to be seen, as does whether the young coder success story becomes a narrative formula in a capacity similar to the Edisonade. However, as the history of the web indicates, the evolution of both fact and fiction might well be linked in an interesting way. Each story of startup success (real or imagined) can generate a sense that other startups are destined to rise to riches—along with their investors—and while the speculative exercises of fiction can offer buoyant takes of the potential of digital media with little risk, the dangers of buoyant speculative in the real tech world are already documented.

-NMK