Thelwall, M. orcid.org/0000-0001-6065-205X and Kousha, K. (2025) Journal Quality Factors from ChatGPT: More meaningful than Impact Factors? Journal of Data and Information Science. ISSN 2096-157X
Abstract
Purpose: Journal Impact Factors and other citation-based indicators are widely used and abused to help select journals to publish in or to estimate the value of a published article. Nevertheless, citation rates primarily reflect scholarly impact rather than other quality dimensions, including societal impact, originality, and rigour. In response to this deficit, Journal Quality Factors (JQFs) are defined and evaluated. These are average quality score estimates given to a journal’s articles by ChatGPT.
Design/methodology/approach: JQFs were compared with Polish, Norwegian and Finnish journal ranks and with journal citation rates for 1,300 journals with 130,000 articles from 2021 in large monodisciplinary journals in the 25 out of 27 Scopus broad fields of research for which it was possible. Outliers were also examined.
Findings: JQFs correlated positively and mostly strongly (median correlation: 0.641) with journal ranks in 24 out of the 25 broad fields examined, indicating a nearly science-wide ability for ChatGPT to estimate journal quality. Journal citation rates had similarly high correlations with national journal ranks, however, so JQFs are not a universally better indicator. An examination of journals with JQFs not matching their journal ranks suggested that abstract styles may affect the result, such as whether the societal contexts of research are mentioned.
Research limitations: Different journal rankings may have given different findings because there is no agreed meaning for journal quality.
Practical implications: The results suggest that JQFs are plausible as journal quality indicators in all fields and may be useful for the (few) research and evaluation contexts where journal quality is an acceptable proxy for article quality, and especially for fields like mathematics for which citations are not strong indicators of quality.
Originality/value: This is the first attempt to estimate academic journal value with a Large Language Model.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2024 Mike Thelwall et al., published by Sciendo. This work is licensed under the Creative Commons Attribution 4.0 International License. (https://creativecommons.org/licenses/by/4.0) |
Keywords: | ChatGPT; Large Language Models; Journal Impact Factors |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 02 Jan 2025 11:42 |
Last Modified: | 03 Feb 2025 10:38 |
Status: | Published online |
Publisher: | Sciendo |
Refereed: | Yes |
Identification Number: | 10.2478/jdis-2025-0016 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:220732 |
Download
Filename: Journal-Quality-Factors-from-ChatGPT-More-meaningful-than-Impact-Factors.pdf
Licence: CC-BY 4.0