On the Entropement of COVID-19 (an Entropology)

The manner in which media have been reporting on the current Covid-19 epidemic has been unique, insofar as the new crisis quickly focused the western media-scape on this one single story in a very short span of time. This effect is highly unusual and differs from the standard tempo and manner of reporting and can yield some unique insights. This essay will attempt to situate the Covid-19 pandemic within a general ‘entropology’, showing how the crisis is being spun in the months following the first reports, and will focus on the manner in which the event of the disease has become ‘entroped’ within contemporary information networks – entropement will be shown to consist in the framing of a “dissipative” object-signifier within a network of parasitic relations.

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Memory Algorithms: Duolingo as a Platform for Second Language Acquisition

The Duolingo mobile app provides a particular case study for encountering the overlap between symbolic and machine language systems in SLA (Second Language Acquisition). Although Duolingo is built upon both natural language as a trésure des significant,[1] as well as the syntactical system of machine code which underwrites the application’s backend processes and frontend interfaces, its pedagogic pipeline bears certain particular features and underlying logics which structure the user’s experience. These features will be analysed presently.  

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Computational Thinking Through the ‘Digital Humanities Stack’

Computational Thinking Revisited

Computational thinking was originally framed as an educational and largely entrepreneurial skill set. Jeannette M. Wing defines it as “an approach to solving problems, designing systems and understanding human behavior that draws on concepts fundamental for computers.”[1] Wing intended this set of thinking through computers to permeate educational facilities, from basic school to university, fostering a new generation able to work within computer environments.[2] There is however a tension in her understanding of computing as “the automation of our abstractions” – a fundamental component of computational thinking since Aho and Ullmann’s Foundations of Computer Science (1992) – and her call to “define the right abstraction.”[3] This critical distance from what the machine can do to what the machine should do has been a central issue in critiques of computational thinking during the last decade.[4] If the user automates her abstractions based on what the machine can do, does she not lose sight of what the machine ought to be doing for the benefit of a wider ecology of relations? If goal orientation is involutional, the critique goes, we stand to lose much of what machines could be doing.

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