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        Evidential Pluralism in the Social Sciences

        Proposal review

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        Author(s)
        Shan, Yafeng
        Williamson, Jon
        Language
        English
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        Abstract
        This volume contends that Evidential Pluralism—an account of the epistemology of causation, which maintains that in order to establish a causal claim one needs to establish the existence of a correlation and the existence of a mechanism—can be fruitfully applied to the social sciences. Through case studies in sociology, economics, political science and law, it advances new philosophical foundations for causal enquiry in the social sciences. The book provides an account of how to establish and evaluate causal claims and it offers a new way of thinking about evidence-based policy, basic social science research and mixed methods research. As such, it will appeal to scholars with interests in social science research and methodology, the philosophy of science and evidence-based policy.
        URI
        https://library.oapen.org/handle/20.500.12657/62477
        Keywords
        causality;correlation;economics;evidence;evidence-based policy;evidential pluralism;law;legal studies;mechanism;mixed methods research;psychology;research methods;science;social science;sociology
        DOI
        10.4324/9781003143000
        ISBN
        9781000890365, 9780367697228, 9781003143000, 9780367697266
        Publisher
        Taylor & Francis
        Publisher website
        https://taylorandfrancis.com/
        Publication date and place
        2023
        Grantor
        • University of Kent
        Imprint
        Routledge
        Series
        Philosophy and Method in the Social Sciences,
        Classification
        Social research and statistics
        Philosophy
        Pages
        191
        Rights
        https://creativecommons.org/licenses/by-nc-nd/4.0/
        • 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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