Defamiliarization: Flarf, conceptual writing, and using flawed software tools as creative partners

doi:10.34105/j.kmel.2012.04.013

Cited by 3 publications

(1 citation statement)

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“…The defamiliarization approach was carried out in many literary studies such as research on novels and songs by Toni Morrison which were rich in metaphor and symbolism (Vegge 2000). Even the technique of defamiliarization was juxtaposed with software for creating creative writing (Gabriel 2012).…”

Section: Discussionmentioning

confidence: 99%

Dynamics of Industrial Revolution 4.0: Digital Technology Transformation and Cultural Evolution

Wulandari¹,

Resmadi²,

Haristianti³

et al. 2021

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Section: Discussionmentioning

confidence: 99%

Dynamics of Industrial Revolution 4.0: Digital Technology Transformation and Cultural Evolution

Wulandari¹,

Resmadi²,

Haristianti³

et al. 2021

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Miscomputation in software: Learning to live with errors

Petříček

2017

Programming

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Computer programs do not always work as expected. In fact, ominous warnings about the desperate state of the software industry continue to be released with almost ritualistic regularity. In this paper, we look at the 60 years history of programming and at the different practical methods that software community developed to live with programming errors.We do so by observing a class of students discussing different approaches to programming errors. While learning about the different methods for dealing with errors, we uncover basic assumptions that proponents of different paradigms follow. We learn about the mathematical attempt to eliminate errors through formal methods, scientific method based on testing, a way of building reliable systems through engineering methods, as well as an artistic approach to live coding that accepts errors as a creative inspiration.This way, we can explore the differences and similarities among the different paradigms. By inviting proponents of different methods into a single discussion, we hope to open potential for new thinking about errors. When should we use which of the approaches? And what can software development learn from mathematics, science, engineering and art?When programming or studying programming, we are often enclosed in small communities and we take our basic assumptions for granted. Through the discussion in this paper, we attempt to map the large and rich space of programming ideas and provide reference points for exploring, perhaps foreign, ideas that can challenge some of our assumptions. ACM CCS 2012General and reference → Surveys and overviews;Social and professional topics → History of computing; The history and classification of errorsIf trials of three or four simple cases have been made, and are found to agree with the results given by the engine, it is scarcely possible that there can be any error among the cards [5].The opening quote from Charles Babbage about the Analytical Engine suggests that Babbage did not see errors as a big problem. If that was the case, the software industry would save billions of dollars, but sadly, eradicating all software errors turned out to be harder than expected.In retrospect, it is curious to see how long it took early computer personnel to realise that coding errors are a problem. One of the first people to realize that " [he] was going to spend a good deal of [his] time finding mistakes that [he] had made in [his] programs" was Maurice Wilkes in late 1940s [17]. However, as Mark Priestley writes, a typical comment [circa 1949] was that of Miller, who wrote that such errors, along with hardware faults, could be "expected, in time, to become infrequent" [41]. While hardware faults did become relatively infrequent, the same cannot be said about coding errors.Over the next 20 years, computers developed from a research experiment into an ordinary scientific tool and industrial instrument. As noted by Nathan Ensmenger, by the end of 1960s many were talking of a computing crisis. For the next "several decades, managers, academic...

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Algorithmic Mimesis: Translation, Technology, Resistance

Slessor

Voyer

2018

ttr

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Translation technologies often figure translation as a simple process of linguistic transfer from one code to another or as a question of selecting the correct matching segments from a database. The prominence of such technologies in the digital age has thus renewed discussions of fidelity and equivalence for translators. The critical attention given to broader cultural and textual contexts that came into focus with the cultural turn seems at risk of disappearing into cyberspace. However, the ongoing proliferation of textual production and reproduction also foregrounds the possibilities of variability and difference in repetition. Using the foibles of technology as catalysts for their own creative ventures, digital-age artists such as Urayoán Noel and Malinda Kathleen Reese channel deficiencies productively in their art, revealing the unsuspected potentials of digital technologies. Such a view of translation as creation challenges the commonplace notion that translation is a scientific act of “carrying across,” a purely semantic transfer that results in the (illusion of) identicality of source and target. Echoing Lévi-Strauss’s notion of “bricolage”—the means by which people retrieve and recombine cultural materials to create new content—Reese and Noel shatter the semantic shackles of identicality by using technology to retrieve and transform the material scraps of language and culture. Their art helps us reconceptualize translation and go beyond fixed notions of what a translation should be or do in terms of fidelity and equivalence. Their playful misuse of machine translation and voice-recognition software allows for a critical analysis of the tension between the universal and the particular as it relates to the act of translation, and does so in a way that uses formal experimentation and humour to resist traditional power dynamics.

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Defamiliarization: Flarf, conceptual writing, and using flawed software tools as creative partners

Cited by 3 publications

References 29 publications

Dynamics of Industrial Revolution 4.0: Digital Technology Transformation and Cultural Evolution

Dynamics of Industrial Revolution 4.0: Digital Technology Transformation and Cultural Evolution

Miscomputation in software: Learning to live with errors

Algorithmic Mimesis: Translation, Technology, Resistance

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