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Fluent in Social Media, Failing in Fake News: Generation Z, Online - Pacific Standard - 0 views

  • Instead of burrowing into a silo or vertical on a single webpage, as our Gen Z digital natives do, fact checkers tended to read laterally, a strategy that sent them zipping off a site to open new tabs across the horizontal axis of their screens. And their first stop was often the site we tell kids they should avoid: Wikipedia. But checkers used Wikipedia differently than the rest of us often do, skipping the main article to dive straight into the references, where more established sources can be found. They knew that the more controversial the topic, the more likely the entry was to be "protected," through the various locks Wikipedia applies to prevent changes by anyone except high-ranking editors. Further, the fact checkers knew how to use a Wikipedia article's "Talk" page, the tab hiding in plain sight right next to the article—a feature few students even know about, still less consult. It's the "Talk" page where an article's claims are established, disputed, and, when the evidence merits it, altered.
  • In the short term, we can do a few useful things. First, let's make sure that kids (and their teachers) possess some basic skills for evaluating digital claims. Some quick advice: When you land on an unfamiliar website, don't get taken in by official-looking logos or snazzy graphics. Open a new tab (better yet, several) and Google the group that's trying to persuade you. Second, don't click on the first result. Take a tip from fact checkers and practice click restraint: Scan the snippets (the brief sentence accompanying each search result) and make a smart first choice.
  • What if the answer isn't more media literacy, but a different kind of media literacy?
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  • We call them "digital natives." Digitally naive might be more accurate.Between January of 2015 and June of 2016, my colleagues and I at the Stanford History Education Group surveyed 7,804 students across 12 states. Our goal was to take the pulse of civic online reasoning: students' ability to judge the information that affects them as citizens. What we found was a stunning and dismaying consistency. Young people's ability to navigate the Internet can be summed up in one word: bleak.
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The Digital-Humanities Bust - The Chronicle of Higher Education - 0 views

  • To ask about the field is really to ask how or what DH knows, and what it allows us to know. The answer, it turns out, is not much. Let’s begin with the tension between promise and product. Any neophyte to digital-humanities literature notices its extravagant rhetoric of exuberance. The field may be "transforming long-established disciplines like history or literary criticism," according to a Stanford Literary Lab email likely unread or disregarded by a majority in those disciplines. Laura Mandell, director of the Initiative for Digital Humanities, Media, and Culture at Texas A&M University, promises to break "the book format" without explaining why one might want to — even as books, against all predictions, doggedly persist, filling the airplane-hanger-sized warehouses of Amazon.com.
  • A similar shortfall is evident when digital humanists turn to straight literary criticism. "Distant reading," a method of studying novels without reading them, uses computer scanning to search for "units that are much smaller or much larger than the text" (in Franco Moretti’s words) — tropes, at one end, genres or systems, at the other. One of the most intelligent examples of the technique is Richard Jean So and Andrew Piper’s 2016 Atlantic article, "How Has the MFA Changed the American Novel?" (based on their research for articles published in academic journals). The authors set out to quantify "how similar authors were across a range of literary aspects, including diction, style, theme, setting." But they never cite exactly what the computers were asked to quantify. In the real world of novels, after all, style, theme, and character are often achieved relationally — that is, without leaving a trace in words or phrases recognizable as patterns by a program.
  • Perhaps toward that end, So, an assistant professor of English at the University of Chicago, wrote an elaborate article in Critical Inquiry with Hoyt Long (also of Chicago) on the uses of machine learning and "literary pattern recognition" in the study of modernist haiku poetry. Here they actually do specify what they instructed programmers to look for, and what computers actually counted. But the explanation introduces new problems that somehow escape the authors. By their own admission, some of their interpretations derive from what they knew "in advance"; hence the findings do not need the data and, as a result, are somewhat pointless. After 30 pages of highly technical discussion, the payoff is to tell us that haikus have formal features different from other short poems. We already knew that.
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  • The outsized promises of big-data mining (which have been a fixture in big-figure grant proposals) seem curiously stuck at the level of confident assertion. In a 2011 New Left Review article, "Network Theory, Plot Analysis," Moretti gives us a promissory note that characterizes a lot of DH writing: "One day, after we add to these skeletons the layers of direction, weight and semantics, those richer images will perhaps make us see different genres — tragedies and comedies; picaresque, gothic, Bildungsroman … — as different shapes; ideally, they may even make visible the micro-patterns out of which these larger network shapes emerge." But what are the semantics of a shape when measured against the tragedy to which it corresponds? If "shape" is only a place-holder meant to allow for more-complex calculations of literary meaning (disburdened of their annoyingly human baggage), by what synesthetic principle do we reconvert it into its original, now reconfigured, genre-form? It is not simply that no answers are provided; it is that DH never asks the questions. And without them, how can Moretti’s "one day" ever arrive?
  • For all its resources, the digital humanities makes a rookie mistake: It confuses more information for more knowledge. DH doesn’t know why it thinks it knows what it does not know. And that is an odd place for a science to be.
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