Macroanalysis by Matthew L. Jockers

Author:Matthew L. Jockers [Jockers, Matthew L.]
Language: eng
Format: epub
Publisher: University of Illinois Press
Published: 2015-10-10T19:55:04+00:00

Figure 6.11. Percentage view of category influence

Figure 6.12. Relative view of category influence

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Some micro thoughts…Claudi Guillén writes in Literature as a System that “genre is an invitation to form” (1971, 109). To “form” we must certainly now add “style,” or if style is too broad a term for some tastes, then at the very least we must add “language usage.” The data presented here provide strong evidence for existing notions of individual authorial “fingerprints,” but show further that author signal alone does not account for the variance in the linguistic data. No doubt, a good share of the variance may be attributed to the particular text that the author is writing, and since the text belongs to, is the creation of, an author, it may be right to conflate the author and text signals into one piece of the pie. Having done so, however, we still have another half pie to account for. Both the classification tests and the linear regression tests showed gender to be a bit player. This was especially evident in the linear regression, where gender rises to only 8 percent of the influence. Even when the classifier showed strong accuracy (at 80 percent), it was still magnitudes below the other categories in terms of improvement over chance. Time and genre are clearly the most complicated factors to understand and thus the most interesting to pursue. It is clear that an author's choice of genre plays a role in determining the subject and form that a novel takes, but genre also plays a role in determining the linguistic material from which the content is derived. Some genre forms clearly move writers to employ more prepositions; other genres demand more articles, or more pronouns, and so on. Given the powerful influence of the author signal and the less powerful but still important factors of time and gender, it is difficult to go much further at the macroanalytic scale. What is required to probe the strength of genre further is an environment in which we can control for gender, author, and time and thus truly isolate the genre signal. In short, we need Charles Dickens, Edward Lytton, and Benjamin Disraeli.

These authors are useful because they are all represented in the test corpus, and they share the distinction of each having authored novels in at least three different genres. From Dickens, the test corpus includes one Bildungsroman, one industrial novel, and one Newgate novel; from Lytton, there is one historical novel, one Newgate novel, and one silver-fork novel; from Disraeli, there is one Bildungsroman, one industrial novel, and one silver-fork novel. Before analyzing the contributions from these three authors and five genres, I will begin by exploring a larger subset of my original corpus that includes just the authors of novels in the Bildungsroman and industrial genres. This allows for a rough approximation of how well the data separate according to genre. Using the same linguistic feature set employed previously, all of the text segments of Bildungsroman and industrial novels (two hundred segments from ten different authors) were isolated and compared.

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