The publication indicator of the Finnish research funding system is based on a manual ranking of scholarly publication channels. These ranks, which represent the evaluated quality of the channels, are continuously kept up to date and thoroughly reevaluated every four years by groups of nominated scholars belonging to different disciplinary panels. This expert-based decision-making process is informed by available citation-based metrics and other relevant metadata characterizing the publication channels. The purpose of this paper is to introduce various approaches that can explain the basis and evolution of the quality of publication channels, i.e., ranks. This is important for the academic community, whose research work is being governed using the system. Data-based models that, with sufficient accuracy, explain the level of or changes in ranks provide assistance to the panels in their multi-objective decision making, thus suggesting and supporting the need to use more cost-effective, automated ranking mechanisms. The analysis relies on novel advances in machine learning systems for classification and predictive analysis, with special emphasis on local and global feature importance techniques.

芬兰研究资助系统的出版指标是基于学术出版渠道的手动排名。这些代表渠道评估质量的排名不断更新，并由不同学科小组的提名学者小组每四年重新评估一次。该基于专家的决策过程由可用的基于引用的度量以及表征发布渠道的其他相关元数据提供信息。本文的目的是介绍可以解释出版渠道（即排名）质量的基础和演变的各种方法。这对于学术界很重要，他们的研究工作正在使用该系统进行管理。基于数据的模型，具有足够的准确性，解释等级的高低或等级的变化为小组的多目标决策提供帮助，从而建议并支持使用更具成本效益的自动排名机制的需求。该分析依靠机器学习系统的新进展进行分类和预测分析，并特别强调局部和全局特征重要性技术。