This book critically analyses the value of citation data, altmetrics, and artificial intelligence to support the research evaluation of articles, scholars, departments, universities, countries, and funders. It introduces and discusses indicators that can support research evaluation and analyses their strengths and weaknesses as well as the generic strengths and weaknesses of the use of indicators for research assessment. The book includes evidence of the comparative value of citations and altmetrics in all broad academic fields primarily through comparisons against article level human expert judgements from the UK Research Excellence Framework 2021. It also discusses the potential applications of traditional artificial intelligence and large language models for research evaluation, with large scale evidence for the former. The book concludes that citation data can be informative and helpful in some research fields for some research evaluation purposes but that indicators are never accurate enough to be described as research quality measures. It also argues that AI may be helpful in limited circumstances for some types of research evaluation.

翻译：本书批判性地分析了引文数据、替代计量学与人工智能在支持文章、学者、部门、大学、国家及资助机构研究评估方面的价值。书中介绍并讨论了可用于支持研究评估的各类指标，分析了这些指标的优势与局限，以及将指标用于研究评估的普遍性优缺点。本书通过将引文和替代计量学数据与英国2021年研究卓越框架中文章级别的人工专家评审结果进行对比，为主要学术领域提供了引文与替代计量学比较价值的实证依据。同时探讨了传统人工智能与大语言模型在研究评估中的潜在应用，并对前者提供了大规模实证支持。本书的结论是：在某些研究领域，针对特定研究评估目的，引文数据能够提供有益信息，但任何指标均不足以精确到可被定义为研究质量的度量标准。同时指出，人工智能仅在有限条件下对某些类型的研究评估可能具有辅助作用。