A messy and time-consuming way to create character lists using Stanford's NER tagger
Recently I’ve been trying to teach myself something about network analysis, and I ran across this article by Markus Luczak-Roesch, Adam Grener, and Emma Fenton, called “Not-so-distant reading: A dynamic network approach to literature” (project site) It describes a tool for generating dynamic and static networks of character occurrences in a text using R, which I’m sort of familiar with. The authors direct readers to their Github repo, so I decided to check it out.
Not the right ideas about character
Working on the book – at least for me – is a constant process of trying to figure out which ideas are worth keeping, and which aren’t. Sometimes I hold really tightly to ideas that aren’t worth keeping, or that I just can’t keep, and then these ideas lead me down confusing and time-consuming rabbit holes where I end up learning a lot about how supercomputer processors work or something (actual example from my dissertation research). Cool! – but not really the point.
Flailing to write the book
The book, the book, the book. For a long time after I graduated with my PhD and somehow got a job I wasn’t making any progress on my book at all. In fact, I took a whole year off from even thinking about writing the book during my first year at my old job at Clemson University. And then after that year, I wrote a lot of things that were supposed to be the book but that didn’t feel right. I was cobbling half-baked ideas together and putting band-aids on chapters I didn’t know how to end or begin and glossing over things that seemed important because I couldn’t articulate how they fit with my grand design. But I kept pounding away at it, doing what I do best, which is trying really hard. It wasn’t working. I did a lot of flailing, which is not quite failing, but which can feel like it.
Failing to get an article published
Not much to say about this one: I recently had an article about critical reading practices rejected for publication. I think in this case it was mainly a situation where the article wasn’t the right fit for the venue, but I don’t know for sure. I know rejection is a totally normal part of the job, and this rejection was not mean-spirited or unfair — but it still stings, right?