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Leslie Harris

For Big-Data Scientists, 'Janitor Work' Is Key Hurdle to Insights - NYTimes.com - 0 views

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    New York Times article about some of the challenges of "big data" analysis - particularly the data cleanup needed to make useful inferences.
Todd Suomela

Big data: are we making a big mistake? - 0 views

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    Very good description of the problems that big data claims to solve, but may not actually solve.
jatolbert

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.
Todd Suomela

Beyond buttonology: Digital humanities, digital pedagogy, and the ACRL Framework | Russell | College & Research Libraries News - 0 views

  • Here are a few specific examples you can apply to your instructional design process to help learners with metacognition: Model the metacognitive process during instruction (or in one-on-one consultations) to ask and reflect on big picture questions such as: “What questions can you answer with this tool?” “What can you not do with this tool?” Keep in mind some answers may be simple (e.g., this tool can only work with data in this way, so it is excluded automatically). Also, “Did I get the results I expected? What could I have done differently?” Start with inquiry and build conversations based on the learner’s answers. “Is it the data that does not work? Or is the research question fundamentally wrong to begin with?” Collaborate with faculty to teach together, modelling your practices while demonstrating a specific tool. This could include thinking aloud as you make decisions so learners can self-correct assumptions. Also, be aware of your own expert bias so you can demonstrate how to clear obstacles. Ask learners to specifically define what is difficult for them during the process of instruction. Digital humanities tools are complex and are based on complex methodologies and research questions. By constructing opportunities for learners to self-question as they move from one task to another, they learn to self-assess their progress and adjust accordingly. There are several instructional design activities that promote metacognition: think-pair-share, one minute paper (“share a key concept learned” or “what comes next?”), and case studies.
  • There are specific strategies we can implement to help learners escape the recursive spiral of the liminal state they experience while managing complex digital projects: One of the most challenging aspects of teaching digital tools is forgetting what it is like to be a novice learner. Sometimes being a near-novice oneself helps you better prepare for the basic problems and frustrations learners are facing. But recognizing liminality is a reminder to you as a teacher that the learning process is not smooth, and it requires anticipating common difficulties and regularly checking in with learners to make sure you are not leaving them behind. When meeting with learners one-on-one, make sure to use your in-depth reference interview skills to engage in methods discussions. When a learner is in the liminal state, they are not always able to “see the forest for the trees.” Your directed questions will illuminate the problems they are having and the solutions they had not seen. Pay close attention to the digital humanities work and discussions happening on your own campus, as well as across the academic community. Working through the liminal space may require helping learners make connections to others facing similar problems. Also follow online discussions in order to point your learners to a wide variety of group learning opportunities, such as the active digital humanities community on Slack.9 When designing instructional opportunities, such as workshops and hackathons, pay particular attention to outreach strategies that may bring like-minded learners together, as well as diverse voices. For example, invite the scholar whose project was completed last year to add a more experienced voice to the conversation. By encouraging the formation of learning communities on your campus, you are creating safe spaces to help learners navigate the liminal state with others who may be on the other side of struggling with specific digital project issues. In designing instructional activities, guide learners through visualization exercises that help to identify “stuck” places. Making graphic representations of one’s thoughts (e.g., concept maps) can highlight areas that require clarification.
Todd Suomela

The Internet as existential threat « Raph's Website - 1 views

  • Our medical systems have terrible Internet security… MRI machines you can connect to with USB that still have “admin:password” to gain root access. That’s horrifying, sure, but that’s not an attack at scale. More frightening: we’re busily uploading all our medical records to the cloud. Take down that cloud, and no patients can be treated, because nobody will know what they have, what meds they are on. Software swallows your insulin pumps and your pacemakers. To kill people, all you need is to hack that database, or simply erase it or block access to it. After all, we don’t tend to realize that in an Internet of Things, humans are just Things too. As this software monster has encroached on stuff like election systems, the common reaction has been to go back to paper. So let’s consider a less obvious example. We should be going back to paper for our libraries too! We’ve outsourced so much of our knowledge to digital that the amount of knowledge available in analog has dropped notably. There are less librarians in the fewer libraries with smaller collections than there used to be. If the net goes down, how much reference material is simply not accessible that was thirty years ago? Google Search is “critical cultural infrastructure.” How much redundancy do we actually have? Could a disconnected town actually educate its children? How critical is Google as a whole? If Google went down for a month, I am pretty sure we would see worldwide economic collapse. How much of the world economy passes through Google hosting? How much of it is in GMail? How much is dependent on Google Search, Google Images, Google Docs? The answer is a LOT. And because financial systems are now also JIT, ten thousand corporate blips where real estate agencies and local car washes and a huge pile of software companies and a gaggle of universities and so on are suddenly 100% unable to function digitally (no payroll! no insurance verification!) would absolutely have ripple effects into their suppliers and their customers, and thence to the worldwide economic market. Because interconnection without redundancy increases odds of cascades.
  • But just as critically, governments and state actors seem to be the source of so many of the problems precisely because the Internet is now too many forms of critical infrastructure, and therefore too juicy a target. If software eats everything, then the ability to kill software is the ability to kill anything. Net connectivity becomes the single point of failure for every system connected to it. Even if the Net itself is designed to route around damage, that doesn’t help if it is the single vector of attack that can take down any given target. It’s too juicy a target for the military, too juicy a target for terror, too juicy a target for criminal ransom. The old adage goes “when they came for this, I said nothing. When they came for that…” — we all know it. Consider that the more we hand gleefully over to the cloud because we want convenience, big data, personalization, and on, we’re creating a single thing that can be taken from us in an instant. We’ve decided to subscribe to everything, instead of owning it. When they came for your MP3s, your DVDs, fine,. not “critical infrastructure.” When they came for your resumes, OK, getting closer.
  • As we rush towards putting more and more things “in the cloud,” as we rush towards an Internet of Things with no governance beyond profit motive and anarchy, what we’re effectively doing is creating a massive single point of failure for every system we put in it.
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