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            The Riddle of Literary Quality

            A Computational Approach

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            Auteur
            van Dalen-Oskam, Karina
            Language
            English
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            Résumé
            What is literature? Can we measure ‘literariness’ in texts themselves? The innovative Computational Humanities project The Riddle of Literary Quality asked thousands of Dutch readers for their opinion about contemporary Dutch and translated novels. The public shared which novels they had read, what they really thought of them, and how they judged their quality. Their judgments of the same novels were compared with the results of computational analysis of the books. Using evidence from almost 14,000 readers and building on more textual data than ever before, Van Dalen-Oskam and her team uncovered unconscious biases that shed new light on prejudices many people assumed no longer existed. This monograph explains in an accessible way how the project unfolded, which methods were used, and how the results may change the future of Literary Studies.
            URI
            https://doab-dev.siscern.org/handle/20.500.12854/188573
            Keywords
            Computational Literary Studies, Literary fiction, Bestsellers, Bias, Readability; thema EDItEUR::C Language and Linguistics::CB Language: reference and general::CBV Creative writing and creative writing guides; thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTP Publishing industry and journalism::KNTP1 Publishing and book trade; thema EDItEUR::D Biography, Literature and Literary studies::D Biography, Literature and Literary studies::DS Literature: history and criticism
            DOI
            10.5117/9789048558148
            ISBN
            9789048558148
            Publisher
            Amsterdam University Press
            Publisher website
            www.aup.nl
            Publication date and place
            Amsterdam, 2023
            Pages
            232
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            • logo EUEuropean Union
              This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871069.

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