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    Interpreting language-learning data

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    Contributor(s)
    Leclercq, Pascale (editor)
    Gudmestad, Aarnes (editor)
    Edmonds, Amanda (editor)
    Collection
    Knowledge Unlatched (KU)
    Language
    English
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    Abstract
    This book provides a forum for methodological discussions emanating from researchers engaged in studying how individuals acquire an additional language. Whereas publications in the field of second language acquisition generally report on empirical studies with relatively little space dedicated to questions of method, the current book gave authors the opportunity to more fully develop a discussion piece around a methodological issue in connection with the interpretation of language-learning data. The result is a set of seven thought-provoking contributions from researchers with diverse interests. Three main topics are addressed in these chapters: the role of native-speaker norms in second-language analyses, the impact of epistemological stance on experimental design and/or data interpretation, and the challenges of transcription and annotation of language-learning data, with a focus on data ambiguity. Authors expand on these crucial issues, reflect on best practices, and provide in many instances concrete examples of the impact they have on data interpretation.
    URI
    https://library.oapen.org/handle/20.500.12657/46940
    Keywords
    Language Arts & Disciplines; Linguistics; Biography & Autobiography
    DOI
    https://doi.org/10.5281/zenodo.4032298
    ISBN
    9783961102822
    Publisher
    Language Science Press
    Publisher website
    https://langsci-press.org/
    Publication date and place
    2020
    Imprint
    Language Science Press
    Classification
    Linguistics
    Biography: general
    Rights
    https://creativecommons.org/licenses/by/4.0/legalcode
    • Harvested from KU

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    License

    • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

    Credits

    • logo EU
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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