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13. Code against code: Creative coding as research methodology

Chapter of: Digital Humanities in the India Rim: Contemporary Scholarship in Australia and India(pp. 245–272)

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Metadata
Title13. Code against code
SubtitleCreative coding as research methodology
ContributorCameron Edmond(author)
Tomasz Bednarz(author)
DOIhttps://doi.org/10.11647/obp.0423.13
Landing pagehttps://www.openbookpublishers.com/books/10.11647/obp.0423/chapters/10.11647/obp.0423.13
Licensehttps://creativecommons.org/licenses/by-nc/4.0/
CopyrightCameron Edmond; Tomasz Bednarz;
PublisherOpen Book Publishers
Published on2024-11-06
Long abstractMachine writing—where computing methods are used to create texts—has risen in popularity recently, diversifying and expanding. Machine writing itself could be seen as a subset of the creative coding discipline. Emblematic of the contemporary turn in machine writing is Darby Larson’s Irritant. Impenetrable by traditional reading standards, the text is governed by code. The reader of Irritant faces similar challenges to the Digital Humanities scholar attempting to analyse large textual corpora. As such, Irritant becomes a useful case study for experimenting with reading methodologies. We approach Irritant from a computational criticism perspective, informed by the same creative coding methods that spawned it. Our objective is to reverse engineer Irritant, scraping its repetitions and variables using Python within a live coding environment. We position creative coding as a research methodology itself, especially suited for analysing machine written texts. This chapter details our process of back-and-forth iteration between the researcher and the text. The ‘hacking’ of the text becomes critical practice itself: an engagement with the coded artefact that meets it on even ground. What our analysis finds, however, is more questions. Our exploration of Irritant fails to unravel the novel’s code in the way we planned, but instead reveals more thematic depth. Far from the post-mortem of a failed experiment, this chapter presents creative coding as a research methodology and interrogates its benefits and challenges via the Irritant case study.
Page rangepp. 245–272
Print length28 pages
LanguageEnglish (Original)
Contributors

Cameron Edmond

(author)
Lecturer in Game Development at Macquarie University
https://orcid.org/0000-0002-4670-6418

Dr Cameron Edmond is a Lecturer in Game Development at Macquarie University, with a focus on Teaching Leadership. As a researcher, he is interested in the intersections between creative writing and coding, and how the two may inform each other in classroom settings. He is also passionate about inclusive teaching practices that empower students to forge their own interdisciplinary links. His research encompasses videogame narrative design, algorithmic and AI-powered literature, as well as data storytelling. He maintains a creative practice as a game developer and experimental poet under the name Uncanny Machines.

Tomasz Bednarz

(author)
https://orcid.org/0000-0001-9240-0922

Associate Professor Tomasz Bednarz has previously served as Director and Head of Visualisation at the Expanded Perception & Interaction Centre at UNSW Art & Design and UNSW Computer Science and Engineering. His past roles reflect his conviction of the need for a holistic approach to the wicked problems facing the collation, analytics and display of big data. His approach is expansive and encompasses the use of novel and emerging technologies. Over the last couple of years, he has been involved in a wide range of projects in immersive visualisation, human-computer interaction, computational imaging, image analysis and processing, visualisation, accelerated computing, simulation, modelling, computer graphics, computer games, computational fluid dynamics, machine learning, Artificial Intelligence, and multi-sensors assimilation.

References
  1. Artut, S. (2017). Incorporation of computational creativity in Arts Education: Creative coding as an Art Course. SHS Web of Conferences 37. https://www.doi.org/10.1051/shsconf/20173701028.
  2. Bakels, J-H., et al., (2020). Matching computational analysis and human experience: Performative arts and the Digital Humanities. Digital Humanities Quarterly 14(4). www.digitalhumanities.org/dhq/vol/14/4/000496/000496.html
  3. Bergstrom, I., & Lotto, R.B. (2015). Code bending: A new creative coding practice. Leonardo 48(1), 25–31. https://www.doi.org/10.1162/LEON_a_00934
  4. Burgess, C., et al., (2020). Stamper: An artboard-oriented creative coding environment. CHI EA ‘20L Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, 1–9. https://www.doi.org/10.1145/3334480.3382994
  5. Butler, B., & Larson, D. (2013, September 11). If you build the code, your computer will write the novel. Vice. www.vice.com/en/article/nnqwvd/if-you-build-the-code-your-computer-will-write-the-novel
  6. Christie, A. & Tanigawa, K. (2016). Mapping Modernism’s Z-axis: A Model for Spatial Analysis in Modernist Studies. In S. Ross & J. O’Sullivan (Eds). Reading Modernism with Machines. (pp. 79–107). Palgrave Macmillan. https://doi.org/10.1057/978-1-137-59569-0_4
  7. Coetzee, C. (2023, March 24). Generating a full-length work of fiction with GPT-4. Medium. https://medium.com/@chiaracoetzee/generating-a-full-length-work-of-fiction-with-gpt-4-4052cfeddef3
  8. Digital Scholar. (2024). Omeka. https://omeka.org/
  9. Dobson, J.E. (2019). Critical Digital Humanities: The Search for a Methodology. University of Illinois Press.
  10. Edmond, C. (2016). The poet’s other self: Studying machine writing through the Humanities. Humanity 7, 1–29. novaojs.newcastle.edu.au/hass/index.php/humanity/article/view/46
  11. Edmond, C. (2019). Poetics of the Machine: Machine Writing and The AI Literature Frontier. Macquarie University. Thesis. https://doi.org/10.25949/19436060.v1
  12. Fullwood, M. (2014). Twide and Twejudice at NaNoGenMo 2014. Michelle Fullwood. michelleful.github.io/code-blog/2014/12/07/nanogenmo-2014/
  13. Greenberg, I., et al., (2013). Processing: Creative Coding and Generative Art in Processing 2. Springer.
  14. Hansen, N., et al., (2014). Crafting Code at the Demo-scene. DIS ‘14: Proceedings of the 2014 Conference on Designing Interactive Systems, 35–8. https://www.doi.org/10.1145/2598510.2598526.
  15. Heflin, J. (2020, March 3). How Do You Map an AI Art World? Immerse, immerse.news/how-do-you-map-an-ai-art-world-8beb3e77a52b
  16. Larson, D. (2011). Pigs. Sleeping Fish. web.archive.org/web/20180118034035/http://www.sleepingfish.net/X/070911_Larson/
  17. Larson, D. (2013). Irritant. Dznac Books.
  18. McElroy, J. (2014). Plus. Dznac Books.
  19. Montfort, N. (2016). Exploratory Programming for the Arts and Humanities. MIT Press.
  20. Murphet, J. (2016). Short Story Futures. In D. Head (Ed.). The Cambridge History of the English Short Story. (pp. 598–614). Cambridge University Press.
  21. Mulligan, J. (2021). Computation and Interpretation in Literary Studies. Critical Inquiry 48 (1), 126–143. https://doi.org/10.1086/715982
  22. Nikitina, S. (2012). Hackers as tricksters of the digital age: Creativity in hacker culture. The Journal of Popular Culture 45(1), 133–-52.
  23. Orekhob, B., & Fischer, F. (2020). Neural reading: Insights from the analysis of poetry generated by artificial neural networks. ORBIS Litterarum, 230–46. https://www.doi.org/10.1111/oli.12274.
  24. Perec, G. (1987). Life: A User’s Manual (D. Bellos, Trans.). Godine.
  25. Project Jupyter. Jupyter Notebook. jupyter.org/
  26. Robinson, E.S. (2011). Shift Linguals: Cut-Up Narratives from William S. Burroughs to the Present. Rodopi.
  27. Samuels, L., & McGann, J. (1999). Deformance and interpretation. New Literary History 30(1), 25–56. https://dx.doi.org/10.1353/nlh.1999.0010
  28. Saum-Pascual, A. (2020). Digital creativity as critical material thinking: The disruptive potential of electronic literature. Electronic Book Review. https://www.doi.org/10.7273/grd1-e122
  29. Svensson, P. (2010). The Landscape of Digital Humanities. Digital Humanities Quarterly 4 (1).
  30. Sierra-Paredes, G. (2017). Postdigital synchronicity and syntopy: The manipulation. Neohelicon 44, 27–39. https://www.doi.org/10.1007/s11059-017-0379-8
  31. Sinclair, S. & Rockwell, G. (2024). Voyant Tools. https://voyant-tools.org
  32. Kazemi, D. (2013). NaNoGenMo. nanogenmo.github.io/
  33. Walton, N. (2019). AI Dungeon 2. www.aidungeon.io/
  34. Wardrip-Fruin, N. (2005). Christopher Stratchey: The First Digital Artist? Grand Text Auto. grandtextauto.soe.ucsc.edu/2005/08/01/christopher-strachey-first-digital-artist/
  35. Wood, J., et al., (2018). Design exposition with literate visualization and computer graphics. IEEE Transactions on Visualization 25(1), 759–68. https://www.doi.org/10.1109/TVCG.2018.2864836.