I sort of hate manuscript stemma. Don’t get me wrong, they have their uses, and they take some incredibly diligent and intelligent work (work that I’m really glad someone else is doing so that I don’t have to). But stemma are also one of those devices that are occasionally put to great evil, in my book. They are sometimes used for recensional editing (the worst of evils), used to abject and even reject certain manuscripts deemed worthless based on its distance from an authoritative text, and they’re just plain mind-numbing to try to consume intellectually.
In addition to being put to evil use, stemma are also sometimes misleading, laden with jargon tending to make them indecipherable to non-experts, and just plain confusing.
Take this A MSS stemma, reproduced (absolutely without permission) from A.V.C. Schmidt’s Parallel Text Edition.
Continue reading A Manuscript Stemma: A Reprise
Material Piers is back after a slight summer holiday. I know you weren’t pining because you were enjoying your own last opportunities to …. xxx… whatever it is you do in the summertime.
We last left off with a discussion of data aesthetics, in which I pointed out that the way you present your data is itself imposing interpretations, or at least interpretive structures, onto the “raw” data itself (whatever that means, amirite Lisa Gitelman??).
Equally important to presenting transparent information is defining the parameters of your data. In a science setting, data is only useful insofar as it is replicable by other scientists. They need to know not just the results (i.e. conclusions you draw from your data) of your experiment, but how you interpreted it, what it looked like pre-interpretation (“raw”), but also how you built the data-finding apparatus, and the question the apparatus was designed to answer. If, for example, you use a laser for something, you are only asking your experiment a question answerable through optical data collection.
The very way that data is “collected” (i.e. created, but more on that later) creates limitations to the kinds of answers you can get from your data.
THERE IS NOTHING INHERENTLY OBJECTIVE ABOUT DATA. Continue reading Defining Data Parameters: Pierscentricity