研究评价中的定量方法 引用指标、Altmetrics 和人工智能

Mike Thelwall
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引用次数: 0

摘要

本书批判性地分析了引用数据、altmetrics和人工智能在支持对文章、学者、院系、大学、国家和资助者进行研究评估方面的价值。本书介绍并讨论了可以支持研究评估的指标,分析了这些指标的优缺点,以及使用指标进行研究评估的一般优缺点。本书主要通过与英国《2021 年卓越研究框架》中的文章级人类专家评判进行比较,证明了引文和 Altmetrics 在所有广泛学术领域的比较价值。书中还讨论了传统人工智能和大型语言模型在研究评估中的潜在应用,并提供了前者的大规模证据。本书的结论是,在某些研究领域,引文数据可以为某些研究评价目的提供信息和帮助,但指标的准确性永远不足以被称为研究质量衡量标准。该书还认为,在有限的情况下,人工智能可能有助于某些类型的研究评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Quantitative Methods in Research Evaluation Citation Indicators, Altmetrics, and Artificial Intelligence
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.
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