It seems to have overlap with the themes in the book by Ed Finn "What Algorithms Want - Imagination in the Age of Computing".
Both say that algorithms are intensely studied from a technical perspective. E.g. O(log n) is better than O(n^2), etc.
Their idea is that the algorithms themselves are creating their own "culture" or "reality" and this should be studied through the lens of "humanities" or "sociology" instead of just "mathematics".
E.g. neural net or statistics algorithm computes that Person A is better credit risk than Person B. However, observers notice that Person B is always black and therefore claim that algorithms are (re)creating racial inequality. Or algorithms that provide sentencing guidelines for convicted felons. Or algorithms that diagnose medical problems.
Other writings with somewhat similar themes:
- Cathy O'Neil, "Weapons of Math Destruction - How Big Data Increases Inequality and Threatens Democracy"
- Eli Pariser, "The Filter Bubble"
There doesn't seem a universal term coined that generalizes the ideas in all 4 of those books but nevertheless, I'm sure more and more writers will notice they are talking about similar ideas.
Side observation about language usage... What I notice in all 4 books is that authors are using the word "algorithms" as a catch-all term for "machine learning". They're not really concerned about building-block algorithms such as "quick sort" or "discrete Fourier transform". What they're all talking about is "Facebook machine learning" is imposing X on us, or "Google's machine learning" is making us think Y. For some reason, the word "algorithm" has gained more currency than "machine learning" in these pop science books.
Some books and articles:
 Protocol by Alexander Galloway
 10 PRINT CHR$(205.5+RND(1));:GOTO 10 by Nick Montfort et al
 On "Sourcery" or Code as Fetish by Wendy Hui Kyong Chun
 The Exploit by Alexander Galloway and Eugene Thacker
I can list many more.
There was also a doctoral seminar taught by Alexander Galloway at NYU in 2010 called The Politics of Code. The reason Galloway's name pops up a ton is that he worked with r-s-g.org and has a fair amount of experience coding in addition to his academic credentials in literary theory.
I am not particularly sure I agree with this approach but his use of that particular reference is appropriate in this case. In fact good, since he has a non-standard (or at least nonstandard outside his discipline) usage of the term. Domain-specific jargon that is still too new to have become completely institutionalized.
What's sad about this work is that Kitchin isn't really interested in having a technical discussion. Github is apparently "a code library", and where decompilation should have been mentioned, it is absent.
A technical collaborator would have improved the paper in those ways, and assuredly others.