The<i>Longue Durée</i>of Literary Prestige

Underwood, Ted; Sellers, Jordan

doi:10.1215/00267929-3570634

Cited by 31 publications

(15 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the non-canonical category, each author has up to 13 articles at most; for the majority of authors, the number is <5. These numbers provide independent evidence for the higher degree of prestige (Underwood and Sellers, 2016 ) of canonical authors, in comparison to non-canonical authors.…”

Section: The Jefp Corpusmentioning

confidence: 79%

“…), politics, etc.,” it is also based on “the text, its reading, readership, literary history, [and] criticism,” i.e., the work of art itself in its cultural context (Tötösy de Zepetnek, 1994 , p. 109, cf. also Underwood and Sellers, 2016 ; Koolen et al, 2020 for a discussion of the relationship between text-intrinsic and text-extrinsic factors in the process of canonization). The question arises whether there are any measurable differences between the text categories of interest.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fractality and Variability in Canonical and Non-Canonical English Fiction and in Non-Fictional Texts

Mohseni

Gast²,

Redies

2021

Front. Psychol.

View full text Add to dashboard Cite

This study investigates global properties of three categories of English text: canonical fiction, non-canonical fiction, and non-fictional texts. The central hypothesis of the study is that there are systematic differences with respect to structural design features between canonical and non-canonical fiction, and between fictional and non-fictional texts. To investigate these differences, we compiled a corpus containing texts of the three categories of interest, the Jena Corpus of Expository and Fictional Prose (JEFP Corpus). Two aspects of global structure are investigated, variability and self-similar (fractal) patterns, which reflect long-range correlations along texts. We use four types of basic observations, (i) the frequency of POS-tags per sentence, (ii) sentence length, (iii) lexical diversity, and (iv) the distribution of topic probabilities in segments of texts. These basic observations are grouped into two more general categories, (a) the lower-level properties (i) and (ii), which are observed at the level of the sentence (reflecting linguistic decoding), and (b) the higher-level properties (iii) and (iv), which are observed at the textual level (reflecting comprehension/integration). The observations for each property are transformed into series, which are analyzed in terms of variance and subjected to Multi-Fractal Detrended Fluctuation Analysis (MFDFA), giving rise to three statistics: (i) the degree of fractality () of the fractal spectrum. The statistics thus obtained are compared individually across text categories and jointly fed into a classification model (Support Vector Machine). Our results show that there are in fact differences between the three text categories of interest. In general, lower-level text properties are better discriminators than higher-level text properties. Canonical fictional texts differ from non-canonical ones primarily in terms of variability in lower-level text properties. Fractality seems to be a universal feature of text, slightly more pronounced in non-fictional than in fictional texts. On the basis of our results obtained on the basis of corpus data we point out some avenues for future research leading toward a more comprehensive analysis of textual aesthetics, e.g., using experimental methodologies.

show abstract

Section: The Jefp Corpusmentioning

confidence: 79%

Section: Introductionmentioning

confidence: 99%

Fractality and Variability in Canonical and Non-Canonical English Fiction and in Non-Fictional Texts

Mohseni

Gast²,

Redies

2021

Front. Psychol.

View full text Add to dashboard Cite

show abstract

“…The statistical model used in this paper is one commonly used by Ted Underwood and others in recent literary scholarship. 104 The model is a logistic regression on word frequencies, optimized to predict whether a novel was published by a nonprofit or conglomerate firm. 105 To ensure the model's generality, it was regularized using an L2-penalty (C = 0.001) and a restricted vocabulary (5000 most frequent words); optimal values for regularization were selected by five-fold cross validation.…”

Section: Appendix Amentioning

confidence: 99%

Against Conglomeration

Sinykin¹,

Roland²

2021

Journal of Cultural Analytics

View full text Add to dashboard Cite

In the 1980s, anxiety about the extensive and ongoing conglomeration of the publishing industry led to the emergence of a movement of nonprofit publishers. It included counterculture figures like Coffee House's Allan Kornblum and Milkweed's Emilie Buchwald, who got their start with boutique letterpresses; political and aesthetic activists like Arte Público's Nicolás Kanellos, Feminist Press's Florence Howe, and Dalkey Archive's John O'Brien; and refugees from conglomeration like Fiona McCrae and André Schiffrin. Nonprofits often defined themselves by their support for literariness, and which they depicted as under threat from commercial houses, which helped them gain support from private foundations, philanthropists, and government agencies like the National Endowment for the Arts. We discovered that these two different ways of structuring publishers' finances-conglomerate and nonprofit-created a split within literature, yielding two distinct modes of American writing after 1980. This essay characterizes the two modes, explains how the split between them happened, and illustrates the significance of this shift for the rise of multiculturalism. We pay particularly close attention to the careers of

show abstract

“…Prior works have shown that stylistic traits to be useful features to predict success of books (Ashok et al, 2013;Underwood and Sellers, 2016;. Ashok et al (2013) used stylistic features extracted using the first 1K sentences from books to classify highly successful literature from less successful literature.…”

Section: Related Workmentioning

confidence: 99%

A Genre-Aware Attention Model to Improve the Likability Prediction of Books

Maharjan

Montes

González

et al. 2018

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

View full text Add to dashboard Cite

Likability prediction of books has many uses. Readers, writers, as well as the publishing industry, can all benefit from automatic book likability prediction systems. In order to make reliable decisions, these systems need to assimilate information from different aspects of a book in a sensible way. We propose a novel multimodal neural architecture that incorporates genre supervision to assign weights to individual feature types. Our proposed method is capable of dynamically tailoring weights given to feature types based on the characteristics of each book. Our architecture achieves competitive results and even outperforms state-of-the-art for this task.

show abstract

TheLongue Duréeof Literary Prestige

Cited by 31 publications

References 21 publications

Fractality and Variability in Canonical and Non-Canonical English Fiction and in Non-Fictional Texts

Fractality and Variability in Canonical and Non-Canonical English Fiction and in Non-Fictional Texts

Against Conglomeration

A Genre-Aware Attention Model to Improve the Likability Prediction of Books

Contact Info

Product

Resources

About