Open Large Language Models for Scientometrics
Special Issue Editor
Interests: scientometrics; bibliometrics; altmetrics; social network analysis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues:
Large language models (LLMs) are increasingly used in all areas of science. These models are trained on vast amounts of text data and use deep learning techniques to generate human-like responses, understand language, and perform a wide range of text-based tasks. This special issue aims to explore the use of open LLMs in advancing the field of scientometrics. Open LLMs refer to LLMs that are made accessible to the public in some form, typically through open-source licenses or publicly available application programming interfaces.
The “open” aspect can vary from fully open-source models that allow users to inspect, modify, and/or deploy them freely to more restrictive, partially open models that may provide access to pre-trained versions but restrict fine-tuning or commercial use. Some well-known open LLMs include DeepSeek, Llama, Gemma, Phi, and Mistral. They serve as alternatives to proprietary models, fostering transparency and collaboration in LLM-supported research.
The focus of this special issue is intentionally broad. Contributions that examine the theoretical foundations of integrating open LLMs into scientometric research, presenting practical use cases, or discuss potential challenges, ethical issues, and policy implications are welcome.
Dr. Robin Haunschild
Dr. Robin Haunschild
Guest Editor
Manuscript Submission Information
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Keywords
- Large language models
- Scientometrics
- Artificial intelligence
- Open science
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