Here is a thought experiment. Make the following assumptions about a historically diverse collection of texts:
1) I have classified them according to genre myself, and trust these classifications.
2) I have classified the items according to time of composition, and I trust these classifications.
So, my items are both historically and generically diverse, and I want to understand this diversity in a new way.
The metadata I have now allows me to partition the set. The partition, by decade, items, and genre class (A, B, C) looks like this:
Decade 1, 100 items: A, 25; B, 50; C, 25
Decade 2, 100 items: A, 30; B, 40; C, 30
Decade 3, 100 items: A, 30; B, 30; C, 40
Decade 4, 100 items: A, 40; B, 40; C, 20
Each decade is labeled (D1, D2 D3) and each contains 100 items. These items are classed by Genre (A, B, C) and the proportions of items belonging to each genre changes from one decade to the next. What could we do with this collection partitioned in this way, particularly with respect to changes in time?
I am interested in genre A, so I focus on that: how does A’ness change over time? Or how does what “counts as A” change over time? I derive a classifier (K) for A in the first Decade and use this distance metric to arrange all items in this decade with respect to A’ness. So my new description allows me to supply the following information about every item: Item 1 participates in A to this degree, and A’ness means “not being B or C in D1.” Let’s call this classifier D1Ka. I can now derive the set of all classifiers with respect to these metadata: D1Ka, D1Kb, D1Kc, D2Ka, D2Kb, etc. And let’s say I derive a classifier for A using the whole dataset. So we add DKa, DKb, DKc. What are these things I have produced and how can they be used to answer interesting questions?
I live in D1, and am confident I know what belongs to A having seen lots of examples. But I get access to a time travel machine and someone sends me a text written much later in time. It is a visitor from D4, and by my own lights, it looks like another example of A. So, I have projected D1Ka onto an item from D4 and made a judgment. Now we lift the curtain and find that for a person living in D4, the item is not an A but a B. Is my classifier wrong? Is this type of projection illegitimate? I don’t think so. We have learned that classifiers themselves have postmarks, and these postmarks are specific to the population in which they are derived. D1Ka is an *artifact* of the initial partitioning of my data: if there were different proportions of A, B, and C within D1, or different items in each of these categories, the classifier would change.
Experiment two. I live in D4 and I go to a used bookstore, where I find a beautifully preserved copy of an item produced in D1. The title page of the this book says, “The Merchant of Venice, a Comedy.” Nonsense, I say. There’s nothing funny about this repellent little play. So D1Ka fails to classify an A for someone in D4. Why? Because the classifier D4Ka is rigidly determined by the variety of the later population, and this variety is different from that found in D1. When classifiers are themselves rigidly aligned with their population of origin, they generalize in funny ways.
Wait, you say. I have another classifier, namely Ka produced over the entire population, which represents all of the time variation in the dataset of 400 items. Perhaps this is useful for describing how A’ness changes over time? Could I compare D1Ka, D2Ka, D3Kz and D4Ka to one another using DKa as my reference? Perhaps, but you have raised a new question: who, if anyone, ever occupies this long interval of time? What kind of abstraction or artifact is DKa, considering that most people really think 10 years ahead or behind when they classify a book? If we are dealing with 27 decades (as we do in the case of our latest big experiment), we have effectively created a classifier for a time interval that no one could ever occupy. Perhaps there is a very well-read person who has read something from each decade and so has an approximation of this longer perspective: that is the advantage of the durability of print, the capacity of memory, and perhaps the viability of reprinting, which in effect imports some of the variation from an earlier decade into a newer one. When we are working with DKa, everything is effectively written at the same time. Can we use this strange assumption — everything is written at once — to explore the real situation, which is that everything is written at a different time?
Another interesting feature of the analysis. This same type of “all written at the same time” reasoning is occurring in our single decade blocks, since when we create the metadata that allows us to treat a subpopulation of texts and belonging to *a* decade, we once again say they were written simultaneously. We use obvious untruths to get at underlying truths, like an astronomer using the inertial assumption to calculate forces, even though we’ve never seen a body travel in a straight line forever.
If classifiers are artifacts of an arbitrarily scalable partitioning of the population, and if these partitions can be compared, what is the ideal form of “classifier time travel” to use when thinking about how actual writing is influenced by other writing, and how a writer’s memory of texts produced in the past can be projected forward into new spaces? Is there anything to be learned about genre A by comparing the classifiers that can be produced to describe it over time? If so, whose perspective are we approximating, and what does that implied perspective say about our underlying model of authorship and literary history?
If classifiers have postmarks, when are they useful in generalizing over — or beyond — a lifetime’s worth of reading?