The Categorically Disaggregated Conflict (CDC) Dataset

The Categorically Disaggregated Conflict (CDC) Dataset provides a categorization of 331 intrastate armed conflicts recorded between 1946 and 2010 into four categories: 1. Ethnic governmental; 2. Ethnic territorial; 3. Non-ethnic governmental; 4. Non-ethnic territorial. The dataset uses the UCDP/PRIO Armed Conflict Dataset v.4-2011, 1946 – 2010 (Themnér & Wallensteen, 2011; also Gleditsch et al., 2002) as a base (and thus is an extension of the UCDP/PRIO dataset). The CDC dataset has been presented in Bartusevicius (2016) (see citation below). A copy of the abstract from the article: Conflict researchers have increasingly stressed the importance of distinguishing between different categories of civil conflict, such as ethnic vs non-ethnic. However, the data on conflict categories has remained limited. This paper introduces the Categorically Disaggregated Conflict (CDC) dataset, which categorizes conflicts based on the two most commonly used distinctions, ethnic-vs-non-ethnic and governmental-vs-territorial, resulting in four conflict categories: ethnic governmental, ethnic territorial, non-ethnic governmental and non-ethnic territorial. While not the first of its kind, the CDC contains a number of novel features. Aside from its unique conceptualization of ethnic conflict, the CDC provides coding of the key component variables (language, religion and “race”), allowing users to re-code ethnic/non-ethnic conflicts into several alternative lists (e.g. religious/non-religious). Furthermore, the CDC provides detailed descriptions documenting coding choices for every single conflict, allowing users to track individual coding decisions. To demonstrate the value of the CDC, this paper replicates a recent study by Cederman, Gelditsch and Buhaug, based on the ACD2EPR—the only extant alternative to the CDC. The findings of the replication analysis challenge some of the key conclusions of the original study, substantiating the need for alternative categorically disaggregated datasets.分类细分冲突（Categorically Disaggregated Conflict, CDC）数据集将1946年至2010年间记录的331起国内武装冲突划分为四类：1. 族裔-政府型；2. 族裔-领土型；3. 非族裔-政府型；4. 非族裔-领土型。本数据集以UCDP/PRIO武装冲突数据集v.4-2011（1946–2010）（Themnér与Wallensteen, 2011；另见Gleditsch等, 2002）为基础，因此是UCDP/PRIO数据集的延伸。CDC数据集由Bartusevicius (2016) 提出（引用信息详见下文）。该论文的摘要内容如下：冲突研究领域的学者日益强调区分不同类型内战的重要性，例如族裔冲突与非族裔冲突，但当前关于冲突类型的相关数据仍较为有限。本文推出分类细分冲突（CDC）数据集，基于学界最常用的两大区分维度——族裔与非族裔、政府诉求与领土诉求，将冲突划分为前述四类。尽管并非首个同类数据集，CDC具备多项创新特性：除了对族裔冲突的独特概念界定外，该数据集还对核心构成变量（语言、宗教与“种族”）进行编码，允许使用者将族裔/非族裔冲突重新编码为多种替代分类（如宗教/非宗教冲突）。此外，CDC为每一起冲突提供了编码决策的详细说明，便于使用者追溯具体的编码过程。为验证CDC的应用价值，本文基于ACD2EPR——目前仅存的CDC替代数据集，复刻了Cederman、Gleditsch与Buhaug的近期研究。复刻分析的结果对原研究的若干核心结论提出了挑战，证实了开发替代性分类细分数据集的必要性。