Published December 21, 2024
| Version 1.0
Dataset
Open
Research on the Impact Evaluation of Academic Monographs Based on Large Language Models
Authors/Creators
Contributors
Researcher (4):
Supervisor:
Description
Based on the abstract text data of 2,248 sociology monographs included in the Dimensions database from 2014 to 2023, the study scores the abstracts using the ChatGPT-4o model and conducts empirical analyses by combining citation counts, Altmetrics data, and readability indices. The results show that the ChatGPT-4o scores have a strong correlation with the number of citations and Altmetrics indicators, outperforming the correlation between the readability index and citations/Altmetrics. This study provides a new framework for the textual analysis of academic monographs and demonstrates the potential of large language models in academic evaluation.
Files
Files
(2.6 MB)
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md5:39642cf581aa8c95b6e0629184194272
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md5:6ce75578170a25ac183eebbde57613e1
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9.4 kB | Download |
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9.1 kB | Download |
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md5:01daade4ddd15da1978e3855bd1366fb
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9.7 kB | Download |
Additional details
Software
- Programming language
- Python