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        The Specificity Space Model

        An Application to the Case of Spain

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        Author(s)
        Lemmer, Daniel
        Collection
        AG Universitätsverlage
        Language
        English
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        Abstract
        Dieses Werk bietet ein alternatives Modell und eine neue Methode zu den Arbeiten der mandate theory und des saliency approaches, um issue positions aus Wahlprogrammen politischer Parteien zu extrahieren. Dieses einfache räumliche Modell besteht in seiner Mitte aus dem Status Quo. Je spezifischer die Wahlversprechen der Parteien in ihren Wahlprogrammen, desto mehr weichen diese vom Status Quo ab. Basierend auf diesem Modell wurde eine neue Methode entwickelt, um issue positions aus Wahlprogrammen zu extrahieren und laut ihrer Spezifizität zu kategorisieren. Eine Anwendung des Modells und der Methode auf die Wahlprogramme der größten spanischen Parteien von 2008 bis 2019 und die Analyse der resultierenden Daten liefern erste tentative Belege dafür, dass die Methode funktioniert und interessante Einsichten in das Verhalten von Parteien unter sich verändernden strategische Bedingungen generieren kann.
        URI
        https://library.oapen.org/handle/20.500.12657/103763
        Keywords
        Parteienwettbewerb; party behavior; Wahlprogramm; issue positions; Parteiensystem; Issue-Orientierung
        DOI
        10.25593/978-3-96147-671-8
        ISBN
        9783961476718, 9783961476701
        Publisher
        FAU University Press
        Publisher website
        https://www.university-press.fau.de/
        Publication date and place
        Erlangen, 2023
        Series
        FAU Studien aus der Philosophischen Fakultät, 25
        Classification
        Political parties and party platforms
        Pages
        506
        Rights
        All rights reserved
        • Imported or submitted locally

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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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