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Journal ArticleDOI

The Longue Durée of Literary Prestige

01 Sep 2016-Modern Language Quarterly (Duke University Press)-Vol. 77, Iss: 3, pp 321-344
TL;DR: The authors studied the stylistic differences associated with literary prominence across a century and found that there is a steady tendency for new volumes of poetry to change by slightly exaggerating certain features that defined prestige in the recent past.
Abstract: A history of literary prestige needs to study both works that achieved distinction and the mass of volumes from which they were distinguished. To understand how those patterns of preference changed across a century, we gathered two samples of English-language poetry from the period 1820–1919: one drawn from volumes reviewed in prominent periodicals and one selected at random from a large digital library (in which the majority of authors are relatively obscure). The stylistic differences associated with literary prominence turn out to be quite stable: a statistical model trained to distinguish reviewed from random volumes in any quarter of this century can make predictions almost as accurate about the rest of the period. The “poetic revolutions” described by many histories are not visible in this model; instead, there is a steady tendency for new volumes of poetry to change by slightly exaggerating certain features that defined prestige in the recent past.

Summary (3 min read)

The Plan of the Experiment

  • The authors could have checked where the books were reviewed; they didn't need computers to guess for us, and they didn't really care whether computers were good at guessing.
  • So although the authors recorded reviewers' sentiments when they were clear, this article places more emphasis on the fact that an author was reviewed at all.
  • The Athenaeum was influential but reviewed so many novels that it was not a sign of great distinction to be included there.
  • The authors also needed a sample that contained books reviewed less often.

A Model of Reception

  • The authors goal here is to assess the strength of the relationship between poetic language and reception.
  • It could be largely accidental; in effect, the algorithm has only "memorized" the quirks of particular examples.
  • If the authors consider publication date as a factor and use the slanted black line to divide the data set, the model is 79.2 percent accurate.
  • Scholars propose different dates, but almost everyone agrees that poetic standards changed dramatically in the nineteenth century.
  • If a single list of words can predict literary prestige across that distance, some aspect of reception must be more stable than the authors anticipated.

The Logic of Poetic Distinction

  • Since the canonical literary tradition seems too diverse to produce this kind of stable boundary, the authors suspected at first that the source of stability must be located in their random sample.
  • A list of the top ten words that individually have the largest effect on the model's predictions might not tell us very much.
  • If it were actually judging poems, the model would be wrong about that line, by the way: the dissolution of imagined immediacy into painful memory at the end is beautiful in context, and it is apt that a poem called "Echo" ends with repetition.
  • So these are the broad patterns that leap out immediately from a model of poetic reception 1820-1919: a preference for concrete language and a relatively dark tone (or at least not a sentimentally uplifting one) (fig. 2 ).
  • One advantage of distant reading is that it can be more patient with historicism, revealing even slow changes as historical phenomena.

How Quickly Does Reception Change?

  • Both articles contrast prominent and obscure works to discover a system of differences that defines literary success.
  • If their models predict that boundary reliably, the authors know that they have captured something important about reception; if one model can predict the boundary reasonably well across a century, they know that some important aspects of reception changed slowly.
  • Poetry itself may have changed in other ways: D. H. Lawrence writes things about the sex lives of whales that would have made Alfred Tennyson blush.
  • In practice, the textual differences associated with success seem to have changed slowly.
  • The authors could train a model only on volumes from one quarter century but ask it to make predictions about the rest of the century.

Synchronic Distinction and Diachronic Change

  • Nevertheless there were many changes, some of them even visible in the figures above.
  • In all these models, the median probability that a volume will be reviewed appears to increase across time.
  • And this is not the only kind of change that can happen in literary history: many different changes are always happening, and many of them won't be captured by a model of distinction.
  • The authors just suggest that, whenever scholars do define a linguistic proxy for social distinction in a given period, they will find that change relative to that axis moves in an upward direction during the period itself.
  • There are ways for us to untangle this causal knot.

Gender and Nationality

  • The methodology the authors are using is close to social science, and they need to be alert for the interactions between variables that preoccupy social scientists.
  • If women were less likely to be reviewed, their model might confound literary prestige with masculinity.
  • The model's predictions for women are just as accurate as those for men, and if the authors run the modeling process on a data set restricted to women, it works just as well.
  • Concrete, troubling images still make poets more successful.
  • American authors are overrepresented in the random sample, and their works are probably more obscure, on the whole, than randomly selected works by British writers.

What Became of Our Original Hypothesis?

  • The original goal of this experiment was to test whether reviewed and random samples would become easier to differentiate as time passed.
  • Critical tradition suggested that distinctions between popular and elite poetic culture had hardened "over the course of the nineteenth century, as the increasingly centralized media and entertainment industries interacted with the growth of education" (Gray 2001: 347) .
  • The authors had planned to begin in 1840, because they didn't expect the style associated with elite taste to be clearly distinct in the period 1840-59 (perhaps a model would be only 60 percent accurate).
  • Figure 1 shows that reviewed and random volumes are more evenly mixed before 1840; accuracy in that section of the timeline is only 66.7 percent.
  • In other words, the early part of the timeline is not just organized by a stylistic boundary different from the one established later; there really appears to be less consensus about stylistic prestige in the first twenty years.

Conclusion

  • A lot of descriptive work remains to be done in literary history, because the authors still know relatively little about patterns above the scale of a few hundred books.
  • Literary historians have often generalized about the pace of change, for instance-contrasting epochs of relative stability to the "revolutions" that separate them (Fallis 1976; Greenblatt et al. 2006 Greenblatt et al. : 1834)) .
  • But those claims are based on limited evidence.
  • The explanation the authors need may have to cover not just poetry but nineteenthcentury literary prestige more generally.
  • 16 Literary scholars haven't described the history of reception very fully yet; it shouldn't be surprising that the authors cannot yet fully explain it.

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01 Jan 2017

2 citations


Cites methods from "The Longue Durée of Literary Presti..."

  • ...Drawing on Braithwaite (2013), we have re-configured the program code that underpins a recent article by Ted Underwood and Jordan Sellers (Underwood & Sellers 2016) into a computational narrative—echoing Knuth’s literate programming (1984)—to be critically analyzed and annotated....

    [...]

Journal ArticleDOI
TL;DR: In this article , the authors present tools, methodologies and practices that offer new possibilities in the study of book history and the history of reading using Google Books Ngram Viewer.
Abstract: The availability of databases of digitised literary materials, such as Google Books, Europeana and historical newspaper databases, has revolutionised many disciplines, e.g., linguistics and history. So far, the use of digitised materials has not been very frequent in the history of books and the history of reading. This article presents tools, methodologies and practices that offer new possibilities in the study of book history and the history of reading. The use of these tools makes it possible to study vast amounts of data quickly and effectively, to present results in helpful visualisations, to make it possible to follow the line of reasoning and, if necessary, to check the reliability of the research by presenting the data for control. The examples presented are drawn from the Google Books database using a simple piece of software that exploits the API of the Google Books Ngram Viewer tool that is available free of charge.

1 citations

DOI
TL;DR: This paper analyzed the influence of genre on literary consecration in the Democratic People's Republic of Korea (DPRK) using quantitative methods and data sets covering a significant part of the country's literary and intellectual output over forty years (1977-2016).
Abstract: Abstract This article analyzes questions of literary production and literary value in the Democratic People's Republic of Korea (DPRK) using quantitative methods and data sets covering a significant part of the country's literary and intellectual output over forty years (1977–2016). The first part of the article adopts a thematic approach, using topic modeling and time series, to map the DPRK's literary output and investigate the influence of genre on literary consecration. The second part looks at questions of style and literary quality in the DPRK through a comparative analysis of multiple novels grouped by degree of literary prestige. It introduces a method to evaluate the originality of an author's writing style using a masked language model (BERT) and uses it to assess the role of conformity and originality in the DPRK's literary field. Even though writers consecrated by the state display a high level of stylistic conformity when writing mandated biographies of the ruling family, their independent output ranks higher in originality than nonconsecrated writers.
References
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"The Longue Durée of Literary Presti..." refers methods in this paper

  • ...5 We used the logistic regression functions from Pedregosa et al. 2011 and visualized results using Wickham 2009....

    [...]

Book
13 Aug 2009
TL;DR: This book describes ggplot2, a new data visualization package for R that uses the insights from Leland Wilkisons Grammar of Graphics to create a powerful and flexible system for creating data graphics.
Abstract: This book describes ggplot2, a new data visualization package for R that uses the insights from Leland Wilkisons Grammar of Graphics to create a powerful and flexible system for creating data graphics. With ggplot2, its easy to: produce handsome, publication-quality plots, with automatic legends created from the plot specification superpose multiple layers (points, lines, maps, tiles, box plots to name a few) from different data sources, with automatically adjusted common scales add customisable smoothers that use the powerful modelling capabilities of R, such as loess, linear models, generalised additive models and robust regression save any ggplot2 plot (or part thereof) for later modification or reuse create custom themes that capture in-house or journal style requirements, and that can easily be applied to multiple plots approach your graph from a visual perspective, thinking about how each component of the data is represented on the final plot. This book will be useful to everyone who has struggled with displaying their data in an informative and attractive way. You will need some basic knowledge of R (i.e. you should be able to get your data into R), but ggplot2 is a mini-language specifically tailored for producing graphics, and youll learn everything you need in the book. After reading this book youll be able to produce graphics customized precisely for your problems,and youll find it easy to get graphics out of your head and on to the screen or page.

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Abstract: There are two cultures in the use of statistical modeling to reach conclusions from data. One assumes that the data are generated by a given stochastic data model. The other uses algorithmic models and treats the data mechanism as unknown. The statistical community has been committed to the almost exclusive use of data models. This commitment has led to irrelevant theory, questionable conclusions, and has kept statisticians from working on a large range of interesting current problems. Algorithmic modeling, both in theory and practice, has developed rapidly in fields outside statistics. It can be used both on large complex data sets and as a more accurate and informative alternative to data modeling on smaller data sets. If our goal as a field is to use data to solve problems, then we need to move away from exclusive dependence on data models and adopt a more diverse set of tools.

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"The Longue Durée of Literary Presti..." refers background in this paper

  • ...If this works, we know that our model has captured a truly generalizable relationship between language and reception (Breiman 2001)....

    [...]

  • ...Compare Breiman 2001 to Shmuéli 2010....

    [...]