Greater collaboration generally produces higher category normalised citation impact (CNCI) and more influential science. Citation differences between domestic and international collaborative articles are known, but obscured in analyses of countries’ CNCIs, compromising evaluation insights. Here, we address this problem by deconstructing and distinguishing domestic and international collaboration types to explore differences in article citation rates between collaboration type and countries. Using Web of Science article data covering 2009–2018, we find that individual country citation and CNCI profiles vary significantly between collaboration types (e.g., domestic single institution and international bilateral) and credit counting methods (full and fractional). The ‘boosting’ effect of international collaboration is greatest where total research capacity is smallest, which could mislead interpretation of performance for policy and management purposes. By incorporating collaboration type into the CNCI calculation, we define a new metric labelled Collab-CNCI. This can account for collaboration effects without presuming credit (as fractional counting does). We recommend that analysts should: (1) partition all article datasets so that citation counts can be normalised by collaboration type (Collab-CNCI) to enable improved interpretation for research policy and management; and (2) consider filtering out smaller entities from multinational and multi-institutional analyses where their inclusion is likely to obscure interpretation.

加强合作通常会产生更高类别的归一化引用影响（CNCI）和更具影响力的科学。国内和国际合作文章之间的引用差异是已知的，但是在对国家CNCI的分析中却被掩盖了，从而损害了评估见解。在这里，我们通过解构和区分国内外协作类型来解决此问题，以探索协作类型与国家之间文章引用率的差异。使用科学网在涵盖2009-2018年的文章数据中，我们发现，在协作类型（例如，国内单一机构和国际双边机构）和信用计数方法（完整和不完整）之间，各个国家的引用和CNCI概况存在显着差异。在总研究能力最小的情况下，国际合作的“推动”效果最大，这可能会误导对政策和管理目的的绩效解释。通过将协作类型合并到CNCI计算中，我们定义了一个名为Collab-CNCI的新指标。这可以在不假定功劳的情况下说明协作效果（就像分数计算一样）。我们建议分析师应：（1）对所有文章数据集进行分区，以便可以通过协作类型（Collab-CNCI）对引用计数进行归一化，以改进对研究政策和管理的解释；（2）考虑从跨国和多机构分析中滤除较小的实体，因为这些实体的加入可能会混淆解释。