I realize this advice may only be relevant to a few souls in this degraded world, but I wanted to document an issue I had when using the org-mode LaTeX exporter with biblatex and James Clawson’s MLA style package.
My template automatically exports babel as one of the LaTeX headers when exporting from org. If the default language is set as “en,” the org exporter will append “,english” as a babel option.
A problem that many of the co-citation graphs I discussed in the last post share is that they are too dense to be easily readable. I created the sliders as a way of alleviating this problem, but some of the data sets are too dense at any citation-threshold. Being able to view only one of the communities at a time seemed like a plausible solution, but I was far from sure how to implement it using d3.
I’ve written here and here about creating co-citation networks in D3 from Web of Science data. My first experiment, described above, was creating a threshold slider. I next wanted to try to create a chronological slider that would allow you to adjust the dates of the citations in the network.
There are doubtless many ways of going about doing this, and I’m reasonably sure that the method I’m going to describe is far from ideal.
I wanted to modify this script by Neal Caren to create an adjustable graph that allows you to control the threshold of citations for nodes that will appear on the graph. If for example, you wanted to see only those nodes with twenty or more citations, you can just move the slider over to see those, and the data will automatically update. I have created three of these: Modernist Journals, Literary Theory, and Rhetoric and Composition.
One of my secret vices is reading polemics about whether or not some group of people, usually humanists or librarians, should learn how to code. What’s meant by “to code” in these discussions varies quite a lot. Sometimes it’s a markup language. More frequently it’s an interpreted language (usually python or ruby). I have yet to come across an argument for why a humanist should learn how to allocate memory and keep track of pointers in C, or master the algorithms and data structures in this typical introductory computer science textbook; but I’m sure they’re out there.
I’ve been thinking a lot recently about a simple question: can machine learning detect patterns of disciplinary change that are at odds with received understanding? The forms of machine learning that I’ve been using to try to test this—LDA and the dynamic LDA variant—do a very good job of picking up the patterns that you would suspect to find in, say, a large corpus of literary journals. The model I built of several theoretically oriented journals in JSTOR, for example, shows much the same trends that anyone familiar with the broad contours of literary theory would expect to find.
No one likes gamification or MOOCs, as far as I can tell. What I should say is that anyone trained in the hermeneutics of suspicion might even find it hard to accept their existence. It’s hard to come up with a hypothetical concept that would cry more piteously to the heavens for critique, for example. True to form, until a few weeks ago I had never earned a badge in my life and would have regarded the prospect of doing so with contempt and a touch of pity for whoever was naive enough to suggest it.