论文标题
科学科学 - 引文模型和研究评估
Science of science -- Citation models and research evaluation
论文作者
论文摘要
从几个角度研究了科学中的引用，其中包括科学学和科学等方法。在本章中，我简要回顾了一些有关引用，引文分布和引文模型的文献。这些引用在涉及研究评估以及指标和指标在此过程中的作用的另一部分中显着。在这里，我在研究评估中简要回顾了部分讨论。这也涉及引用与同行评审的关系。最后，我通过试图整合这两种文献来得出结论。研究评估的基本问题是研究质量是无法观察的。这对我们可以从引用和引文模型的定量研究中得出的结论产生了影响。在这种情况下，``指标''一词是一个相关的概念，我试图澄清。因果关系对于正确理解指标很重要，尤其是在实践中使用指标时：当我们采取指标时，我们进入因果领域。即使指示器可能通过其非常使用，其使用的后果也可能使其无效。通过将引文模型与适当的因果推理相结合，并认识到有关无法观察的研究质量的基本问题，我们可能希望取得进步。
Citations in science are being studied from several perspectives, among which approaches such as scientometrics and science of science. In this chapter I briefly review some of the literature on citations, citation distributions and models of citations. These citations feature prominently in another part of the literature which is dealing with research evaluation and the role of metrics and indicators in that process. Here I briefly review part of the discussion in research evaluation. This also touches on the subject of how citations relate to peer review. Finally, I conclude by trying to integrate the two literatures. The fundamental problem in research evaluation is that research quality is unobservable. This has consequences for conclusions that we can draw from quantitative studies of citations and citation models. The term ``indicators'' is a relevant concept in this context, which I try to clarify. Causality is important for properly understanding indicators, especially when indicators are used in practice: when we act on indicators, we enter causal territory. Even when an indicator might have been valid, through its very use, the consequences of its use may invalidate it. By combining citation models with proper causal reasoning and acknowledging the fundamental problem about unobservable research quality, we may hope to make progress.
