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        Chapter 32 Discourse Network Analysis

        Proposal review

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        Author(s)
        Eder, Franz
        Language
        English
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        Abstract
        This chapter introduces Discourse Network Analysis (DNA). DNA argues that actors are communicative agents that discursively seek to promote their policy preferences in a contested domestic political arena, by building coalitions of like-minded. Thus, DNA bridges the gap between Foreign Policy Analysis (FPA) and public policy by integrating domestic politics into the analysis of foreign policy decision-making. DNA rests on two methodological approaches. First, it uses qualitative content analysis for gathering and coding the statements of actors. Second, it utilizes social network analysis to link statements and actors in networks, and thus makes discursive strategies and processes of coalition-building visible. To illustrate the applicability of DNA to FPA, this chapter discusses the debates in the British House of Commons on the Iraq War of 2003.
        Book
        Routledge Handbook of Foreign Policy Analysis Methods
        URI
        https://library.oapen.org/handle/20.500.12657/60272
        Keywords
        political discourse, advocacy coalition framework, qualitative content analysis, social network analysis, domestic turn
        DOI
        10.4324/9781003139850-39
        ISBN
        9781003139850, 9780367689766, 9780367689803
        Publisher
        Taylor & Francis
        Publisher website
        https://taylorandfrancis.com/
        Publication date and place
        2023
        Grantor
        • Universität Innsbruck
        Imprint
        Routledge
        Classification
        Politics and government
        Pages
        21
        Rights
        https://creativecommons.org/licenses/by-nc-nd/4.0/
        • Imported or submitted locally

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        • 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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