# Quantitative Methods in Research Evaluation Citation Indicators, Altmetrics, and Artificial Intelligence

**Authors:** Mike Thelwall  
**arXiv:** [2407.00135](https://arxiv.org/abs/2407.00135)  
**Date:** 2024-06-28  
**Summary page:** https://www.summarizepaper.com/en/paper/2407.00135v1/

## Key points

- The text examines the use of citation data, altmetrics, and artificial intelligence in research evaluation.
- Various indicators are discussed for evaluation at different levels such as articles, scholars, departments, universities, countries, and funders.
- Strengths and weaknesses of these indicators are explored along with their broader implications for research assessment.
- Evidence comparing citations and altmetrics against expert judgments from the UK Research Excellence Framework 2021 is presented to showcase their value across diverse academic fields.
- Potential applications of traditional artificial intelligence and large language models for research evaluation are discussed with supporting evidence of their effectiveness.

## Summary

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"Quantitative Methods in Research Evaluation: Citation Indicators, Altmetrics, and Artificial Intelligence" by Mike Thelwall from the University of Sheffield critically examines the use of citation data, altmetrics, and artificial intelligence in research evaluation. The book discusses various indicators that aid in evaluation at different levels such as articles, scholars, departments, universities, countries, and funders. It also explores the strengths and weaknesses of these indicators and their broader implications for research assessment. Thelwall presents evidence comparing citations and altmetrics against expert judgments from the UK Research Excellence Framework 2021 to showcase their value across diverse academic fields. Furthermore, he delves into the potential applications of traditional artificial intelligence and large language models for research evaluation with supporting evidence of their effectiveness. Ultimately, Thelwall concludes that while citation data can offer valuable insights in certain research fields for specific purposes, they should not be considered as measures of research quality on their own. He also argues that artificial intelligence may have a role in limited circumstances for certain types of research evaluation.