使用配对比较和自适应Lasso估计计算共识期刊元排名
Computing a consensus journal meta-ranking using paired comparisons and adaptive lasso estimators
摘要：
在一种“不成功就灭亡”的文化中，科学期刊的排名在评估当前研究环境中的绩效方面发挥着核心作用。随着各种现有的方法和途径来获得期刊排名，元排名作为一种聚合不同信息源的手段已经越来越受欢迎。在本文中，我们提出了一种方法来创建一个共识元排名使用异构期刊排名。使用配对比较数据的参数模型，我们估计在OR/MS社区的58种期刊的质量分数，它与收缩过程一起允许识别具有相似质量的期刊群。配对比较的使用提供了一个灵活的框架，用于导出共识评分，同时消除数据缺失的问题。
英文摘要：
In a "publish-or-perish culture", the ranking of scientific journals plays a central role in assessing performance in the current research environment. With a wide range of existing methods and approaches to deriving journal rankings, meta-rankings have gained popularity as a means of aggregating different information sources. In this paper, we propose a method to create a consensus meta-ranking using heterogeneous journal rankings. Using a parametric model for paired comparison data we estimate quality scores for 58 journals in the OR/MS community, which together with a shrinkage procedure allows for the identification of clusters of journals with similar quality. The use of paired comparisons provides a flexible framework for deriving a consensus score while eliminating the problem of data missingness.
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