MLA Convention 2020: Documenting a Graduate Course in Electronic Literature with ScalarMain MenuMLA Convention 2020: Documenting a Graduate Course in Electronic Literature with ScalarAcknowledgmentNazua IdrisIntroductionKathryn ManisDesigner's StatementNazua IdrisChapter 1: Responding to Major Theoretical Works of Electronic LiteratureSection I: "Intimate Mechanics: One Model of Electronic Literature"Kathryn ManisSection II: "Future Fiction Storytelling Machines"Nazua IdrisSection III: "Digital Interventions"Nazua IdrisSection IV: "Teaching Electronic Literature as Digital Humanities: A Proposal"Ricardo RamirezSection V: "Feminism, Print, Machines"Ricardo RamirezSection VI: "On Turbulence"Ricardo RamirezSection VII: "Literary Gaming"Ricardo RamirezSection VIII: "The Machine in the Text, and the Text in the Machine"Landon RoperSection IX: "Literary Texts as Cognitive Assemblages: The Case of Electronic Literature"Landon RoperChapter 2: Critical Engagements with Electronic LiteratureSection I: "The Ballad of Sand and Harry Soot" by Stephanie StricklandKathryn ManisSection II: "Patchwork Girl" by Shelley JacksonKathryn ManisSection III: "Faith" by Robert KendallNazua IdrisSection IV: “Loss of Grasp” by Serge BouchardonNazua IdrisSection V: "Shy boy" by Thom SwissRicardo RamirezSection VI: "RedRidingHood" by Donna LeishmanRicardo RamirezSection VII: "Tipoemas y Anipoemas" by Ana Maria UribeLandon RoperSection VIII: "Dakota" by Young Hae-Chang Heavy IndustriesLandon RoperChapter 3: Pedagogical Possibilities: Electronic Literature in Classroom and BeyondSection I: At the Intersection of Games and E-Lit: Kathryn Manis in conversation with Nicholas BinfordKathryn ManisSection II: Group Traversal on Judd Morrissey's "The Jew's Daughter"Nazua IdrisSection II: Individual Case StudiesJulian Ankney's CaseNicholas Binford's CaseTroy Rowden's CaseRichard Snyder's CaseRosamond Thalken's CaseConclusionsRicardo RamirezAuthors' BiosNazua IdrisLandon Roperd6bafe98ae021bac254d2976714bb17c121d306b
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1media/23270461_705422317532_5366879737887638277_o_thumb.jpg2020-01-03T20:17:25-08:00Nazua Idrisbc2d1d8ad5bf3aaef0a149de2b46feb78e7486a3328471PhD Candidate, Literary Studies, Washington State Universityplain2020-01-03T20:17:25-08:00Nazua Idrisbc2d1d8ad5bf3aaef0a149de2b46feb78e7486a3
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1media/23753065302_26da63dc0e_b.jpgmedia/twitter_screen_social_phone_cellular_phone_technology_editorial_illustrative-1028513.jpg!d.jpg2020-01-03T19:36:48-08:00Richard Snyder's Case14image_header2020-01-06T17:03:58-08:00Richard Snyder Doctoral Candidate (Literary Studies) Department of English, Washington State University Email: richard.snyder@wsu.edu Website: RDSnyder.info “Ovalbot Alpha” Artist Statement
This Python project was inspired by combinatory poems such as Stochastic Texts by Theo Lutz and Taroko Gorge by Nick Montfort, which use algorithms and random selection in order to output poetry which is different each time it is read. Ovalbot is a combinatory advice bot which lives on Twitter and may be found at the URL above. Given the current occupant of the White House’s apparent unfamiliarity with or disregard for many of the formal and informal procedures and considerations of that office, Ovalbot seeks to harness the voices of past Presidents of the United States in order to give the current President some guidance. This is a somewhat playful enterprise, to be sure, but it also seeks to provide a serious commentary on the decorum and deep rhetorical responsibilities of the office. The guidance provided by Ovalbot comes in the form of past Presidents’ own words, taken from many of their most famous speeches, such as the Gettysburg Address (Bliss copy), JFK’s Inaugural speech, and Lyndon B. Johnson’s speech on voting rights. The bot works by first acquiring President Trump’s latest tweet, and splitting it into individual characters. Its main algorithm then uses that set of character as a key upon which to base its selection of words taken from past presidential speeches as outlined above, piecing them together in a format which simulates advice or truisms, and randomizing sentence structure to allow for modifying each noun with adjectives or tying the statements together with conjunctions. Here are a few samples of the statements that it has produced:
“selfish offices render callous public voices”
“freedoms influence evanescent men”
“great suffrages carry convictions”
“virtues appear to me arduous inclinations”
“powers lead to sacrifices”
Producing and launching such a project required deepening my knowledge of Python and algorithmic processes, making well-informed curatorial choices about source material, and learning how to build, authorize, and host a Twitter bot. This was a lot to accomplish over the course of only a few weeks for someone with minimal programming experience, so the project is imperfect. Ovalbot is not automated at the moment—it runs only when I prompt it to do so. The code is also not optimized and many of the passages end prematurely, a problem which I intend to fix when I rewrite the bot in Javascript in 2020. That said, I am very happy with its current state, given my time and resources, and it was a rich and wonderful learning experience to build Ovalbot 1.0. You will find a few samples of the Python code below.