Original
Studying Advocacy Coalitions: Conceptual Choices and Methodological Approaches
Abstract
ABSTRACT Advocacy Coalition Framework (ACF) research has been continuously evolving to improve the understanding of coalition studies. This study aims to critically examine the most common methods for coalition identification in ACF research and to identify strategies to strengthen their clarity and interpretation. We identify and develop ideas around four key steps in using social network analysis (SNA) to study coalitions: collecting and understanding data, choosing a community detection algorithm, applying data transformations, and, most importantly, interpreting community structures. We argue that neither this paper nor any others can provide a definitive approach for understanding advocacy coalitions. Instead, our charge to those using the ACF is threefold. First, recognize the elusiveness of advocacy coalitions and the inherent limitations of any representation. Second, acknowledge that network analysis of advocacy coalitions involves numerous combinatorial choices determined by data characteristics, algorithm selection, and data transformation choices. Third, encourage researchers to understand their data, select among these combinations, interpret results, and effectively communicate the sensitivity and robustness of their analyses.
中文
研究倡导联盟：概念选择与方法路径
摘要
摘要：倡导联盟框架（ACF）研究一直在持续演进，以增进对联盟研究的理解。本研究旨在批判性审视ACF研究中用于识别联盟的最常见方法，并识别可增强其清晰性与解释力的策略。我们围绕使用社会网络分析（SNA）研究联盟的四个关键步骤提出并发展相关思路：收集与理解数据、选择社区发现算法、应用数据变换，以及最重要的——解释社区结构。我们认为，无论是本文还是其他研究，都无法为理解倡导联盟提供一种确定性的方法。相反，我们对ACF使用者的要求有三点。第一，认识到倡导联盟的难以捉摸性以及任何表征所固有的局限。第二，承认对倡导联盟的网络分析涉及大量组合性选择，这些选择由数据特征、算法选择和数据变换决定。第三，鼓励研究者理解其数据，在这些组合中进行选择，解释结果，并有效传达其分析的敏感性与稳健性。
关键词
倡导联盟框架、倡导联盟、社会网络分析、社区发现算法、联盟识别、方法论、政策过程理论、分析稳健性