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Kayvan Kousha

Publications and source records attributed to Kayvan Kousha.

At least 19 recordsLinked to original sources
arXiv2026

Does ChatGPT score research quality differently by gender?

Large Language Models (LLMs) are being considered for research evaluation, raising concerns about the introduction of AI bias. This study investigates whether ChatGPT research quality scores differ by first-author gender using 89,744 journal articles from the UK Research Excellence Framework (REF) 2021. Author information was withheld from ChatGPT to avoid direct gender bias. Nevertheless, male first-authored papers had slightly higher ChatGPT scores in most Units of Assessment (UoAs), especially in health, science and engineering-related subjects, and this pattern was often stronger for ChatGPT than for REF scores, based on a departmental-level proxy. Rank-based ChatGPT gains relative to REF scores were also more favourable for male first-authored papers in most UoAs, although the differences were generally small. Gender differences were not evident for solo research in the social sciences, arts and humanities, however. The male-favouring pattern for first-authored research was not explained by gender differences in writing styles, at least as reflected in abstract complexity. Some ChatGPT-REF differences may also reflect the departmental averaging process used to generate the REF proxy scores. Average ChatGPT scores may differ by first-author gender indirectly through other factors, such as field, topic, method, journal context or authorship structure. Thus, this is an additional reason to be cautious with AI-based research evaluation.

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arXiv2026

Have LLM-associated terms increased in article full texts in all fields?

The use of Large Language Models (LLMs) like ChatGPT and DeepSeek for translation and language polishing is a welcome development, reducing the longstanding publishing barrier to non-English speakers. Assessing the uptake of this facility is useful to give insights into changing nature of scientific writing. Although the prevalence of LLM-associated terms has been tracked across science in abstracts and for full text biomedical research, their science-wide prevalence in full texts is unknown. In response, this article investigates an expanded set of 80 potentially LLM-associated terms during 2021-2025 in a science-wide full text collection from the publisher MDPI (1.25 million articles), partly focusing on the 73 journals that published at least 500 articles in 2021. The results demonstrate the increasing prevalence of LLM-associated terms science-wide in full texts to 2024, with some terms declining from 2024 to 2025 and others continuing to increase. LLMs seem to avoid some terms (e.g., thus, moreover) and a few terms have stronger associations with abstracts than full texts (e.g., enhanced) or the opposite (e.g., leveraged). The term family "underscore" had the biggest increase: up to 29-fold. There are substantial differences between journals in the apparent use of LLMs for writing, from lower uptake in the life sciences to higher uptake in social sciences, electronic engineering and environmental science. Fields in which there is currently low uptake may need improved or specialist support, such as for reliably translating complex formulae, before the full benefits of automatic translation can be realised.

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arXiv2026

Which stylistic features fool ChatGPT research evaluations?

Large Language Models (LLMs) have the potential to be used to support research evaluation and have a moderate capability to estimate the research quality of a journal article from its title and abstract. This paper assesses whether there are language-related factors unrelated to the quality of the research that influence ChatGPT's scores. Using a dataset of 99,277 journal articles submitted to the UK-wide Research Excellence Framework (REF) 2021 assessments, we calculated several readability indicators from abstracts and correlated them with ChatGPT scores and departmental REF scores. From the results, linguistic complexity and length were more strongly associated with ChatGPT research quality scores than with REF expert scores in many subject areas. Although cause-and-effect was not tested, these results suggest that ChatGPT may be more likely than human experts to reward linguistic complexity, with a potential bias towards longer and less readable abstracts in many fields. The apparent preference of LLMs for complex language is an undesirable feature for practical applications of LLMs for research quality evaluation, unless solutions can be found.

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arXiv2025

Can ChatGPT evaluate research environments? Evidence from REF2021

UK academic departments are evaluated partly on the statements that they write about the value of their research environments for the Research Excellence Framework (REF) periodic assessments. These statements mix qualitative narratives and quantitative data, typically requiring time-consuming and difficult expert judgements to assess. This article investigates whether Large Language Models (LLMs) can support the process or validate the results, using the UK REF2021 unit-level environment statements as a test case. Based on prompts mimicking the REF guidelines, ChatGPT 4o-mini scores correlated positively with expert scores in almost all 34 (field-based) Units of Assessment (UoAs). ChatGPT's scores had moderate to strong positive Spearman correlations with REF expert scores in 32 out of 34 UoAs: 14 UoAs above 0.7 and a further 13 between 0.6 and 0.7. Only two UoAs had weak or no significant associations (Classics and Clinical Medicine). From further tests for UoA34, multiple LLMs had significant positive correlations with REF2021 environment scores (all p < .001), with ChatGPT 5 performing best (r=0.81; $\rho$=0.82), followed by ChatGPT-4o-mini (r=0.68; $\rho$=0.67) and Gemini Flash 2.5 (r=0.67; $\rho$=0.69). If LLM-generated scores for environment statements are used in future to help reduce workload, support more consistent interpretation, and complement human review then caution must be exercised because of the potential for biases, inaccuracy in some cases, and unwanted systemic effects. Even the strong correlations found here seem unlikely to be judged close enough to expert scores to fully delegate the assessment task to LLMs.

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arXiv2025

How much are LLMs changing the language of academic papers after ChatGPT? A multi-database and full text analysis

This study investigates how Large Language Models (LLMs) are influencing the language of academic papers by tracking 12 LLM-associated terms across six major scholarly databases (Scopus, Web of Science, PubMed, PubMed Central (PMC), Dimensions, and OpenAlex) from 2015 to 2024. Using over 2.4 million PMC open-access publications (2021-July 2025), we also analysed full texts to assess changes in the frequency and co-occurrence of these terms before and after ChatGPT's initial public release. Across databases, delve (+1,500%), underscore (+1,000%), and intricate (+700%) had the largest increases between 2022 and 2024. Growth in LLM-term usage was much higher in STEM fields than in social sciences and arts and humanities. In PMC full texts, the proportion of papers using underscore six or more times increased by over 10,000% from 2022 to 2025, followed by intricate (+5,400%) and meticulous (+2,800%). Nearly half of all 2024 PMC papers using any LLM term also included underscore, compared with only 3%-14% of papers before ChatGPT in 2022. Papers using one LLM term are now much more likely to include other terms. For example, in 2024, underscore strongly correlated with pivotal (0.449) and delve (0.311), compared with very weak associations in 2022 (0.032 and 0.018, respectively). These findings provide the first large-scale evidence based on full-text publications and multiple databases that some LLM-related terms are now being used much more frequently and together. The rapid uptake of LLMs to support scholarly publishing is a welcome development reducing the language barrier to academic publishing for non-English speakers.

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arXiv2024

Journal Quality Factors from ChatGPT: More meaningful than Impact Factors?

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 contrast, Journal Quality Factors (JQFs) are average quality score estimates given to a journal's articles by ChatGPT. Design: 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. Limitations: Different journal rankings may have given different findings because there is no agreed meaning for journal quality. 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: This is the first attempt to estimate academic journal value with a Large Language Model.

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arXiv2024

Assessing the societal influence of academic research with ChatGPT: Impact case study evaluations

Academics and departments are sometimes judged by how their research has benefitted society. For example, the UK Research Excellence Framework (REF) assesses Impact Case Studies (ICS), which are five-page evidence-based claims of societal impacts. This study investigates whether ChatGPT can evaluate societal impact claims and therefore potentially support expert human assessors. For this, various parts of 6,220 public ICS from REF2021 were fed to ChatGPT 4o-mini along with the REF2021 evaluation guidelines, comparing the results with published departmental average ICS scores. The results suggest that the optimal strategy for high correlations with expert scores is to input the title and summary of an ICS but not the remaining text, and to modify the original REF guidelines to encourage a stricter evaluation. The scores generated by this approach correlated positively with departmental average scores in all 34 Units of Assessment (UoAs), with values between 0.18 (Economics and Econometrics) and 0.56 (Psychology, Psychiatry and Neuroscience). At the departmental level, the corresponding correlations were higher, reaching 0.71 for Sport and Exercise Sciences, Leisure and Tourism. Thus, ChatGPT-based ICS evaluations are simple and viable to support or cross-check expert judgments, although their value varies substantially between fields.

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arXiv2022

Terms in journal articles associating with high quality: Can qualitative research be world-leading?

Purpose: Scholars often aim to conduct high quality research and their success is judged primarily by peer reviewers. Research quality is difficult for either group to identify, however, and misunderstandings can reduce the efficiency of the scientific enterprise. In response, we use a novel term association strategy to seek quantitative evidence of aspects of research that associate with high or low quality. Design/methodology/approach: We extracted the words and 2-5-word phrases most strongly associating with different quality scores in each of 34 Units of Assessment (UoAs) in the Research Excellence Framework (REF) 2021. We extracted the terms from 122,331 journal articles 2014-2020 with individual REF2021 quality scores. Findings: The terms associating with high- or low-quality scores vary between fields but relate to writing styles, methods, and topics. We show that the first-person writing style strongly associates with higher quality research in many areas because it is the norm for a set of large prestigious journals. We found methods and topics that associate with both high- and low-quality scores. Worryingly, terms associating with educational and qualitative research attract lower quality scores in multiple areas. REF experts may rarely give high scores to qualitative or educational research because the authors tend to be less competent, because it is harder to make world leading research with these themes, or because they do not value them. Originality: This is the first investigation of journal article terms associating with research quality.

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arXiv2022

Artificial intelligence technologies to support research assessment: A review

This literature review identifies indicators that associate with higher impact or higher quality research from article text (e.g., titles, abstracts, lengths, cited references and readability) or metadata (e.g., the number of authors, international or domestic collaborations, journal impact factors and authors' h-index). This includes studies that used machine learning techniques to predict citation counts or quality scores for journal articles or conference papers. The literature review also includes evidence about the strength of association between bibliometric indicators and quality score rankings from previous UK Research Assessment Exercises (RAEs) and REFs in different subjects and years and similar evidence from other countries (e.g., Australia and Italy). In support of this, the document also surveys studies that used public datasets of citations, social media indictors or open review texts (e.g., Dimensions, OpenCitations, Altmetric.com and Publons) to help predict the scholarly impact of articles. The results of this part of the literature review were used to inform the experiments using machine learning to predict REF journal article quality scores, as reported in the AI experiments report for this project. The literature review also covers technology to automate editorial processes, to provide quality control for papers and reviewers' suggestions, to match reviewers with articles, and to automatically categorise journal articles into fields. Bias and transparency in technology assisted assessment are also discussed.

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arXiv2022

Do bibliometrics introduce gender, institutional or interdisciplinary biases into research evaluations?

Systematic evaluations of publicly funded research typically employ a combination of bibliometrics and peer review, but it is not known whether the bibliometric component introduces biases. This article compares three alternative mechanisms for scoring 73,612 UK Research Excellence Framework (REF) journal articles from all 34 field-based Units of Assessment (UoAs) 2014-17: peer review, field normalised citations, and journal average field normalised citation impact. All three were standardised into a four-point scale. The results suggest that in almost all academic fields, bibliometric scoring can disadvantage departments publishing high quality research, with the main exception of article citation rates in chemistry. Thus, introducing journal or article level citation information into peer review exercises may have a regression to the mean effect. Bibliometric scoring slightly advantaged women compared to men, but this varied between UoAs and was most evident in the physical sciences, engineering, and social sciences. In contrast, interdisciplinary research gained from bibliometric scoring in about half of the UoAs, but relatively substantially in two. In conclusion, out of the three potential source of bias examined, the most serious seems to be the tendency for bibliometric scores to work against high quality departments, assuming that the peer review scores are correct. This is almost a paradox: although high quality departments tend to get the highest bibliometric scores, bibliometrics conceal the full extent of departmental quality advantages. This should be considered when using bibliometrics or bibliometric informed peer review.

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arXiv2022

Why are co-authored academic articles more cited: Higher quality or larger audience?

Co-authored articles tend to be more cited in many academic fields, but is this because they tend to be higher quality or is it an audience effect: increased awareness through multiple author networks? We address this unknown with the largest investigation yet into whether author numbers associate with research quality, using expert peer quality judgements for 122,331 non-review journal articles submitted by UK academics for the 2014-20 national assessment process. Spearman correlations between the number of authors and the quality scores show moderately strong positive associations (0.2-0.4) in the health, life, and physical sciences, but weak or no positive associations in engineering, and social sciences. In contrast, we found little or no association in the arts and humanities, and a possible negative association for decision sciences. This gives reasonably conclusive evidence that greater numbers of authors associates with higher quality journal articles in the majority of academia outside the arts and humanities, at least for the UK. Positive associations between team size and citation counts in areas with little association between team size and quality also show that audience effects or other non-quality factors account for the higher citation rates of co-authored articles in some fields.

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arXiv2022

In which fields are citations indicators of research quality?

Citation counts are widely used as indicators of research quality to support or replace human peer review and for lists of top cited papers, researchers, and institutions. Nevertheless, the relationship between citations and research quality is poorly evidenced. We report the first large-scale science-wide academic evaluation of the relationship between research quality and citations (field normalised citation counts), correlating them for 87,739 journal articles in 34 field-based UK Units of Assessment (UoAs). The two correlate positively in all academic fields, from very weak (0.1) to strong (0.5), reflecting broadly linear relationships in all fields. We give the first evidence that the correlations are positive even across the arts and humanities. The patterns are similar for the field classification schemes of Scopus and Dimensions.ai, although varying for some individual subjects and therefore more uncertain for these. We also show for the first time that no field has a citation threshold beyond which all articles are excellent quality, so lists of top cited articles are not pure collections of excellence, and neither is any top citation percentile indicator. Thus, whilst appropriately field normalised citations associate positively with research quality in all fields, they never perfectly reflect it, even at high values.

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arXiv2022

Which international co-authorships produce higher quality journal articles?

International collaboration is sometimes encouraged in the belief that it generates higher quality research or is more capable of addressing societal problems. Nevertheless, while there is evidence that the journal articles of international teams tend to be more cited than average, perhaps from increased international audiences, there is no science-wide direct academic evidence of a connection between international collaboration and research quality. This article empirically investigates the connection between international collaboration and research quality for the first time, with 148,977 UK-based journal articles with post publication expert review scores from the 2021 Research Excellence Framework (REF). Using an ordinal regression model controlling for collaboration, international partners increased the odds of higher quality scores in 27 out of 34 Units of Assessment (UoAs) and all Main Panels. The results therefore give the first large scale evidence of the fields in which international co-authorship for articles is usually apparently beneficial. At the country level, the results suggests that UK collaboration with other high research-expenditure economies generates higher quality research, even when the countries produce lower citation impact journal articles than the United Kingdom. Worryingly, collaborations with lower research-expenditure economies tend to be judged lower quality, possibly through misunderstanding Global South research goals.

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arXiv2022

In which fields do higher impact journals publish higher quality articles?

The Journal Impact Factor and other indicators that assess the average citation rate of articles in a journal are consulted by many academics and research evaluators, despite initiatives against overreliance on them. Despite this, there is limited evidence about the extent to which journal impact indicators in any field relates to human judgements about the journals or their articles. In response, we compared average citation rates of journals against expert judgements of their articles in all fields of science. We used preliminary quality scores for 96,031 articles published 2014-18 from the UK Research Excellence Framework (REF) 2021. We show that whilst there is a positive correlation between expert judgements of article quality and average journal impact in all fields of science, it is very weak in many fields and is never strong. The strength of the correlation varies from 0.11 to 0.43 for the 27 broad fields of Scopus. The highest correlation for the 94 Scopus narrow fields with at least 750 articles was only 0.54, for Infectious Diseases, and there was only one negative correlation, for the mixed category Computer Science (all). The results suggest that the average citation impact of a Scopus-indexed journal is never completely irrelevant to the quality of an article, even though it is never a strong indicator of article quality.

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arXiv2022

Do altmetric scores reflect article quality? Evidence from the UK Research Excellence Framework 2021

Altmetrics are web-based quantitative impact or attention indicators for academic articles that have been proposed to supplement citation counts. This article reports the first assessment of the extent to which mature altmetrics from Altmetric.com and Mendeley associate with journal article quality. It exploits expert norm-referenced peer review scores from the UK Research Excellence Framework 2021 for 67,030+ journal articles in all fields 2014-17/18, split into 34 Units of Assessment (UoAs). The results show that altmetrics are better indicators of research quality than previously thought, although not as good as raw and field normalised Scopus citation counts. Surprisingly, field normalising citation counts can reduce their strength as a quality indicator for articles in a single field. For most UoAs, Mendeley reader counts are the best, tweet counts are also a relatively strong indicator in many fields, and Facebook, blogs and news citations are moderately strong indicators in some UoAs, at least in the UK. In general, altmetrics are the strongest indicators of research quality in the health and physical sciences and weakest in the arts and humanities. The Altmetric Attention Score, although hybrid, is almost as good as Mendeley reader counts as a quality indicator and reflects more non-scholarly impacts.

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arXiv2022

Is Research Funding Always Beneficial? A Cross-Disciplinary Analysis of UK Research 2014-20

The search for and management of external funding now occupies much valuable researcher time. Whilst funding is essential for some types of research and beneficial for others, it may also constrain academic choice and creativity. Thus, it is important to assess whether it is ever detrimental or unnecessary. Here we investigate whether funded research tends to be higher quality in all fields and for all major research funders. Based on peer review quality scores for 113,877 articles from all fields in the UK's Research Excellence Framework (REF) 2021, we estimate that there are substantial disciplinary differences in the proportion of funded journal articles, from Theology and Religious Studies (16%+) to Biological Sciences (91%+). The results suggest that funded research is likely to be higher quality overall, for all the largest research funders, and for all fields, even after factoring out research team size. There are differences between funders in the average quality of the research they support, however. Funding seems particularly beneficial in health-related fields. The results do not show cause and effect and do not take into account the amount of funding received but are consistent with funding either improving research quality or being won by high quality researchers or projects. In summary, there are no broad fields of research in which funding is irrelevant, so no fields can afford to ignore it. The results also show that citations are not effective proxies for research quality in the arts and humanities and most social sciences for evaluating research funding.

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arXiv2022

Predicting article quality scores with machine learning: The UK Research Excellence Framework

National research evaluation initiatives and incentive schemes have previously chosen between simplistic quantitative indicators and time-consuming peer review, sometimes supported by bibliometrics. Here we assess whether artificial intelligence (AI) could provide a third alternative, estimating article quality using more multiple bibliometric and metadata inputs. We investigated this using provisional three-level REF2021 peer review scores for 84,966 articles submitted to the UK Research Excellence Framework 2021, matching a Scopus record 2014-18 and with a substantial abstract. We found that accuracy is highest in the medical and physical sciences Units of Assessment (UoAs) and economics, reaching 42% above the baseline (72% overall) in the best case. This is based on 1000 bibliometric inputs and half of the articles used for training in each UoA. Prediction accuracies above the baseline for the social science, mathematics, engineering, arts, and humanities UoAs were much lower or close to zero. The Random Forest Classifier (standard or ordinal) and Extreme Gradient Boosting Classifier algorithms performed best from the 32 tested. Accuracy was lower if UoAs were merged or replaced by Scopus broad categories. We increased accuracy with an active learning strategy and by selecting articles with higher prediction probabilities, as estimated by the algorithms, but this substantially reduced the number of scores predicted.

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