Pace of Change

The bespoke code we defactor is that which underlies an article that Underwood and Sellers published in Modern Language Quarterly (2016): The Longue Durée of Literary Prestige. This article was the culmination of prior work in data preparation (Underwood and Sellers, 2014), coding (Underwood and Sellers 2015a ad 2018) and preparatory analysis (Underwood and Sellers, 2015). The main thrust of the MLQ article seems to be one of method: “Scholars more commonly study reception by contrasting positive and negative reviews. That approach makes sense if you’re interested in gradations of approval between well-known writers, but it leaves out many works that were rarely reviewed at all in selective venues. We believe that this blind spot matters: literary historians cannot understand the boundary of literary distinction if they look only at works on one side of the boundary” (Underwood and Sellers 2016:324). To substantiate their claim Underwood and Sellers begin their “inquiry with the hypothesis that a widely discussed “great divide” between elite literary culture and the rest of the literary field started to open in the late nineteenth century”. To this end they compare volumes of poetry that were reviewed in elite journals in the period 1820-1917 with randomly sampled volumes of poetry from the HathiTrust Digital Library from the same period. They filtered out volumes from the HathiTrust resource that were written by authors that were also present in the reviewed set, effectively ending up with non-reviewed volumes. In all they compare 360 volumes of ‘elite’ poetry and 360 non-reviewed volumes. For each volume the relative frequencies of the 3200 most common words are tallied and they apply linear regression to these frequency histograms. This linear regression model enables them finally to predict whether a sample that was not part of the regression data would have been reviewed or not. The accuracy of their predictions is between 77.5 and 79.2 percent, which demonstrates that there is some relationship between some poetry volume’s vocabulary and that volume being reviewed. But more importantly, what they can glean from their results is that the traditional idea that literary fashions are pretty stable over some decades and are then revolutionized towards a new fashion deserves revisiting: the big 19th century divide turns out not to be a revolutionary change but a stable and slowly progressing trend since at least since 1840. Underwood and Sellers conclude: none of our “models can explain reception perfectly, because reception is shaped by all kinds of social factors, and accidents, that are not legible in the text. But a significant chunk of poetic reception can be explained by the text itself (the text supports predictions that are right almost 80 percent of the time), and that aspect of poetic reception remained mostly stable across a century”. Sudden changes also do not emerge if they try to predict other social categories like genre or authorial gender. They finally conclude that the question why the general slow trend they see exists is too big to answer from these experiments alone, because of the many social factors that are involved.

Underwood and Sellers purposely divided their code into logical and meaningful parts, modules, and functions stitched together into a data processing and analysis script. We found to better understand the code as readers (vs. authors) and to narrate its execution it was necessary to restructure, defactor, the code into what is usually understood as a poor software engineering practice, namely making a single long, strongly integrated, procedural process. This makes the code a linear narrative, which is easier for humans to read while the computer is for the most part indifferent. There is a tension between these two mutually exclusive representations of narratives with code divided and branched, emerging from the process of development by software engineers, and with prose as a linear narrative intended for a human reader. What we observed is that the processes of deconstructing literature and code are not symmetrical but mirrored. Where deconstructing literature usually involves breaking a text apart into its various components, meanings, and contexts, deconstructing software by defactoring means integrating the code’s disparate parts into a single, linear computational narrative. “Good code,” in other words, is already deconstructed (or ‘refactored’) into modules and composable parts. For all practical purposes we effectively are turning “well engineered” code into sub-optimal code full of ‘hacks’ and terrible ‘code smells’ by de-modularizing it. However, we argue, this “bad” code is easier to read and critique while still functioning as its authors intended.

Defactoring injects the logical sections of the code, parts that execute steps in the workflow, with our own narrative reporting on our understanding of the code and its functioning at that moment of the execution. The Jupyter Notebook platform makes this kind of incremental exploration of the code possible and allows us to present a fully functioning and executable version of Underwood and Sellers’s code that we have annotated. Reading (and executing along the way) this notebook therefore gives the reader a close resembling of the experience of how we as deconstructionists ‘closely read’ the code.¹