For this assignment, due at the beginning of each meeting as indicated in the syllabus, submit a two to three sentence summary of the reading’s argument for that day
For this assignment, due at the beginning of each meeting as indicated in the syllabus, submit a two to three sentence summary of the reading’s argument for that day. After this summary, note five keywords that express the breadth, depth, and/or specificities of those arguments. (There will likely be some overlap between your sentences and keywords.)
Before each summary, include a citation in the format of your choice (you can draw these from the syllabus, but check for errors).
While this may seem quick and easy, it’s more often a frustrating exercise that asks you to read deeply before encapsulating complex arguments in ways that you understand. If you don’t understand the argument(s), do your best.
Always structure your summaries as follows. You don’t have to use these specific words, but they’re popular in academic writing for a reason. Note the third person simple present tense and effort at concision.
The first sentence will look something like:
[Author surname]
argues
[X, Y, Z.]
observes, examines, explores
claims
evaluates
considers
illustrates, highlights
Consider lists of verbs like this one for more options.
There are many ways to mix this up:
Drawing on X and Y, Hall argues that A and B.
Hall claims that A and B, his larger goal is to evaluate C.
Hall illustrates how F impacts/conditions/informs the E of G.
The second and possibly third sentences expand on the first. Academic arguments are often complex, so it can be hard to fit it all in one sentence. If things start getting unwieldy, multiple sentences are fine. The second sentence might give more details of the argument’s organization (often key), definitions, evidence, and how it actualizes the author’s goal.
More specifically, he D and E.
This argument rests on A and B, and Hall explicitly rejects C.
Some examples (note that these are all missing the keywords, which should go after the sentences):
Anderson, C. (2012). Towards a sociology of computational and algorithmic journalism. New Media & Society, 15(7).
Anderson advocates a contextual approach to analyzing “computational journalism,” i.e., journalism as it has come to be influenced by the logics of Big Data: algorithms, automation, high-speed computation, etc. Specifically, he suggests that “internalist” approaches that explore computational journalism with the goal of improving it should be bracketed – at least temporarily – with externalist approaches that explore micro and macro political, economic, institutional, organizational, cultural, and technological contexts. Anderson offers this as an extension to “sociology of news” approaches, though I imagine that cultural studies offers better tools for thinking about contexts.
Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society.
Situating herself among discussions around algorithmic accountability, agency, and visibility, Burrell explores “opacity” in machine learning algorithms and distinguishes between three types: 1) “intentional corporate or state secrecy,” 2) “technical illiteracy,” and 3) mismatches between human and machine cognition. By recognizing the different forms of opacity at play in a specific algorithmic context, clarifying solutions might be more easily realized. That said, this article advances a method that approaches algorithms at a higher level of abstraction by looking at exemplary models of algorithms used in undergraduate education.
Sometimes, your first clause (as above) or even sentence (as below) offers context.
Milan, S. (2015). When algorithms shape collective action: Social media and the dynamics of cloud protesting. Social Media + Society.
Social media affect protest movements and other sorts of collective action, contributing to new processes and forms of identity and sociality. Milan explores how such transformations occur today with the notion of “cloud protesting,” a framework that emphasizes social media’s micro effects, socio-technical (and symbolic-material) nature, and contributions to the formation of collective identity. Milan takes a Winnerian stance to the politics of social media, which she finds embedded in material and symbolic constraints that support particular logics and regimes characterized by centralization, corporatization, vertical integration, “leaky software,” and data collection (p. 3).
Think about What are the relationships among platforms, media, and popular culture? as you Read: Chandler, A. and Krishnamurthy, V. (2018). The myth of platform neutrality. Georgetown Law Technology Review, 2(2), 400-416.
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