Studying Advocacy Coalitions: Conceptual Choices and Methodological Approaches
本文批判性审视倡导联盟框架中识别联盟的常用方法，提出用社会网络分析研究联盟的四个关键步骤，并强调研究者需理解数据、选择算法、解释结果并沟通分析的敏感性与稳健性。
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.